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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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    <title>Spiralator | Spirograph Drawing Tool | Free Online Mobile PC SVG Vector Graphics</title>
    <dc:date>2026-06-16T19:08:33+00:00</dc:date>
    <link>https://spiralator.com/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Spiralator

A spirograph drawing tool. Free, online, mobile-friendly. Designed to help explore and express the beautiful geometries of circles rotating in circles. Inspired by the old spirograph toy but not constrained by its practicalities.

Instructions

The vertical sliders set the disc and pen configuration. Draw by dragging the moving disc around the fixed disc. Double-clicking in the main window starts and stops auto-drawing.

Some tips: Press the "Demo" button to watch a random series of shapes being drawn; when you're experimenting, the "Show Preview" button is useful to immediately see the effects of altering the parameters; "View Gallery" in the "Share/Save" menu for inspiration; for a "light mode", the "Set BG" button enables changing the background using the colour sliders.
]]></description>
<dc:subject>interactive visualization web-applications drawing genetic-programming consider:implicit-equations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1ac57c1dab6d/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
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<item rdf:about="https://arxiv.org/abs/2410.04480">
    <title>[2410.04480] Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation</title>
    <dc:date>2026-05-24T10:59:46+00:00</dc:date>
    <link>https://arxiv.org/abs/2410.04480</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The ability to think abstractly and reason by analogy is a prerequisite to rapidly adapt to new conditions, tackle newly encountered problems by decomposing them, and synthesize knowledge to solve problems comprehensively. We present TransCoder, a method for solving abstract problems based on neural program synthesis, and conduct a comprehensive analysis of decisions made by the generative module of the proposed architecture. At the core of TransCoder is a typed domain-specific language, designed to facilitate feature engineering and abstract reasoning. In training, we use the programs that failed to solve tasks to generate new tasks and gather them in a synthetic dataset. As each synthetic task created in this way has a known associated program (solution), the model is trained on them in supervised mode. Solutions are represented in a transparent programmatic form, which can be inspected and verified. We demonstrate TransCoder's performance using the Abstract Reasoning Corpus dataset, for which our framework generates tens of thousands of synthetic problems with corresponding solutions and facilitates systematic progress in learning.
]]></description>
<dc:subject>ARC machine-learning genetic-programming neural-networks program-synthesis learning-from-data artificial-intelligence rather-interesting hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5ba29e7a72c7/</dc:identifier>
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<item rdf:about="https://link.springer.com/article/10.1007/s11047-025-10028-7">
    <title>Genetic programming policies for bin packing in the framework of deterministic Markov decision process | Natural Computing</title>
    <dc:date>2025-07-24T11:17:41+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11047-025-10028-7</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The Bin Packing Problem (BPP) is a well-known NP-hard problem with numerous real-world applications. This study focuses on minimizing waste and maximum lateness in a one-dimensional version of the BPP, which is particularly relevant in industrial contexts. The goal is to develop a constructive heuristic algorithm that can be adapted to various situations. We model the BPP as a Deterministic Markov Decision Process with discrete state and action spaces, where policies are represented by arithmetic expressions involving state variables. This approach allows for a clearer explanation of the decision process, in contrast to other methods like neural networks. To evolve these policies, we use Genetic Programming (GP). Trained on a set of BPP instances, the resulting policies are effective for solving new, unseen instances. In the experimental study, we explore different GP settings, including varying sets of symbols. The results reveal valuable insights about the importance of state variables, indicating that a smaller selection of them may yield the best results. The evolved policies are compared with an exact method from the literature, achieving similar outcomes but with significantly less computational time.

]]></description>
<dc:subject>operations-research packing genetic-programming rather-interesting deterministic-approximations approximation machine-learning to-write-about consider:heuristic-move</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:19868cd2a237/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2505.21991">
    <title>[2505.21991] Bridging Fitness With Search Spaces By Fitness Supremums: A Theoretical Study on LGP</title>
    <dc:date>2025-06-04T18:35:32+00:00</dc:date>
    <link>https://arxiv.org/abs/2505.21991</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Genetic programming has undergone rapid development in recent years. However, theoretical studies of genetic programming are far behind. One of the major obstacles to theoretical studies is the challenge of developing a model to describe the relationship between fitness values and program genotypes. In this paper, we take linear genetic programming (LGP) as an example to study the fitness-to-genotype relationship. We find that the fitness expectation increases with fitness supremum over instruction editing distance, considering 1) the fitness supremum linearly increases with the instruction editing distance in LGP, 2) the fitness infimum is fixed, and 3) the fitness probabilities over different instruction editing distances are similar. We then extend these findings to explain the bloat effect and the minimum hitting time of LGP based on instruction editing distance. The bloat effect happens because it is more likely to produce better offspring by adding instructions than by removing them, given an instruction editing distance from the optimal program. The analysis of the minimum hitting time suggests that for a basic LGP genetic operator (i.e., freemut), maintaining a necessarily small program size and mutating multiple instructions each time can improve LGP performance. The reported empirical results verify our hypothesis.
]]></description>
<dc:subject>genetic-programming theory-and-practice-sitting-in-a-tree evolutionary-algorithms to-understand to-write-about consider:abstraction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9a8fcf1518c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
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<item rdf:about="https://arxiv.org/abs/2504.07779v1">
    <title>[2504.07779v1] Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems</title>
    <dc:date>2025-04-16T13:34:31+00:00</dc:date>
    <link>https://arxiv.org/abs/2504.07779v1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Dynamic scheduling in real-world environments often struggles to adapt to unforeseen disruptions, making traditional static scheduling methods and human-designed heuristics inadequate. This paper introduces an innovative approach that combines Genetic Programming (GP) with a Transformer trained through Reinforcement Learning (GPRT), specifically designed to tackle the complexities of dynamic scheduling scenarios. GPRT leverages the Transformer to refine heuristics generated by GP while also seeding and guiding the evolution of GP. This dual functionality enhances the adaptability and effectiveness of the scheduling heuristics, enabling them to better respond to the dynamic nature of real-world tasks. The efficacy of this integrated approach is demonstrated through a practical application in container terminal truck scheduling, where the GPRT method outperforms traditional GP, standalone Transformer methods, and other state-of-the-art competitors. The key contribution of this research is the development of the GPRT method, which showcases a novel combination of GP and Reinforcement Learning (RL) to produce robust and efficient scheduling solutions. Importantly, GPRT is not limited to container port truck scheduling; it offers a versatile framework applicable to various dynamic scheduling challenges. Its practicality, coupled with its interpretability and ease of modification, makes it a valuable tool for diverse real-world scenarios.
]]></description>
<dc:subject>scheduling industrial-engineering operations-research rather-interesting genetic-programming representation consider:rediscovery consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2b9c1da6f7fa/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2504.05418v1">
    <title>[2504.05418v1] Evolving Financial Trading Strategies with Vectorial Genetic Programming</title>
    <dc:date>2025-04-16T13:30:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2504.05418v1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Establishing profitable trading strategies in financial markets is a challenging task. While traditional methods like technical analysis have long served as foundational tools for traders to recognize and act upon market patterns, the evolving landscape has called for more advanced techniques. We explore the use of Vectorial Genetic Programming (VGP) for this task, introducing two new variants of VGP, one that allows operations with complex numbers and another that implements a strongly-typed version of VGP. We evaluate the different variants on three financial instruments, with datasets spanning more than seven years. Despite the inherent difficulty of this task, it was possible to evolve profitable trading strategies. A comparative analysis of the three VGP variants and standard GP revealed that standard GP is always among the worst whereas strongly-typed VGP is always among the best.
]]></description>
<dc:subject>genetic-programming symbolic-regression financial-engineering rather-interesting representation typed-GP to-write-about hey-I-know-these-folks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:dee3a271c15c/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-these-folks"/>
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<item rdf:about="https://arxiv.org/abs/2412.08186">
    <title>[2412.08186] Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations</title>
    <dc:date>2024-12-21T14:58:57+00:00</dc:date>
    <link>https://arxiv.org/abs/2412.08186</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Multigrid methods are asymptotically optimal algorithms ideal for large-scale simulations. But, they require making numerous algorithmic choices that significantly influence their efficiency. Unlike recent approaches that learn optimal multigrid components using machine learning techniques, we adopt a complementary strategy here, employing evolutionary algorithms to construct efficient multigrid cycles from available individual components. This technology is applied to finite element simulations of the laser beam welding process. The thermo-elastic behavior is described by a coupled system of time-dependent thermo-elasticity equations, leading to nonlinear and ill-conditioned systems. The nonlinearity is addressed using Newton's method, and iterative solvers are accelerated with an algebraic multigrid (AMG) preconditioner using hypre BoomerAMG interfaced via PETSc. This is applied as a monolithic solver for the coupled equations. To further enhance solver efficiency, flexible AMG cycles are introduced, extending traditional cycle types with level-specific smoothing sequences and non-recursive cycling patterns. These are automatically generated using genetic programming, guided by a context-free grammar containing AMG rules. Numerical experiments demonstrate the potential of these approaches to improve solver performance in large-scale laser beam welding simulations.
]]></description>
<dc:subject>genetic-programming manufacturing applied-mathematics control-theory rather-interesting to-write-about consider:process-control</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0c4ae1074eba/</dc:identifier>
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<item rdf:about="https://link.springer.com/article/10.1007/s10710-023-09469-9">
    <title>Is the evolution metaphor still necessary or even useful for genetic programming? | Genetic Programming and Evolvable Machines</title>
    <dc:date>2024-10-06T12:54:53+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10710-023-09469-9</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[I read with great interest the historical review of Koza’s first book (a.k.a. “Jaws”) on genetic programming (GP) by Langdon. There is no question that this book has had a big impact on research and practice in artificial intelligence (AI), artificial life, and machine learning. It certainly inspired my own lifelong work on developing and applying GP algorithms and software when I first read it as a graduate student at the University of Michigan in the 1990s. In fact, one of my first activities as a new graduate and assistant professor was to implement Koza’s symbolic regression code in LISP and adapt it to do symbolic discriminant analysis with application to biomedical data. The evolution metaphor used by GP, and its predecessor the genetic algorithm, was very appealing to me as a student because it was easy to understand, and my biology training taught me that evolution by natural selection is a powerful tinkerer and problem solver in nature. Now that I have nearly 30 years of experience with GP, I can’t help but question whether the evolution metaphor is still necessary or even useful?

]]></description>
<dc:subject>genetic-programming define-your-terms paradigmatics pragmatism hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c0a75ec338bb/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:paradigmatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s10710-023-09467-x">
    <title>Jaws 30 | Genetic Programming and Evolvable Machines</title>
    <dc:date>2024-10-06T12:54:05+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s10710-023-09467-x</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[It is 30 years since John R. Koza published “Jaws”, the first book on genetic programming [Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)]. I recount and expand the celebration at GECCO 2022, very briefly summarise some of what the rest of us have done and make suggestions for the next thirty years of GP research.

]]></description>
<dc:subject>genetic-programming retrospective JAWS hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b56a002f4930/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:retrospective"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:JAWS"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2305.01582">
    <title>[2305.01582] Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl</title>
    <dc:date>2024-07-25T13:41:42+00:00</dc:date>
    <link>https://arxiv.org/abs/2305.01582</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[PySR is an open-source library for practical symbolic regression, a type of machine learning which aims to discover human-interpretable symbolic models. PySR was developed to democratize and popularize symbolic regression for the sciences, and is built on a high-performance distributed back-end, a flexible search algorithm, and interfaces with several deep learning packages. PySR's internal search algorithm is a multi-population evolutionary algorithm, which consists of a unique evolve-simplify-optimize loop, designed for optimization of unknown scalar constants in newly-discovered empirical expressions. PySR's backend is the extremely optimized Julia library SymbolicRegression.jl, which can be used directly from Julia. It is capable of fusing user-defined operators into SIMD kernels at runtime, performing automatic differentiation, and distributing populations of expressions to thousands of cores across a cluster. In describing this software, we also introduce a new benchmark, "EmpiricalBench," to quantify the applicability of symbolic regression algorithms in science. This benchmark measures recovery of historical empirical equations from original and synthetic datasets.
]]></description>
<dc:subject>symbolic-regression OK-so-what-year-is-it? Python genetic-programming system-of-professions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7abb0573f340/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:OK-so-what-year-is-it?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-of-professions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2402.08011">
    <title>[2402.08011] On The Nature Of The Phenotype In Tree Genetic Programming</title>
    <dc:date>2024-07-12T10:41:27+00:00</dc:date>
    <link>https://arxiv.org/abs/2402.08011</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this contribution, we discuss the basic concepts of genotypes and phenotypes in tree-based GP (TGP), and then analyze their behavior using five benchmark datasets. We show that TGP exhibits the same behavior that we can observe in other GP representations: At the genotypic level trees show frequently unchecked growth with seemingly ineffective code, but on the phenotypic level, much smaller trees can be observed. To generate phenotypes, we provide a unique technique for removing semantically ineffective code from GP trees. The approach extracts considerably simpler phenotypes while not being limited to local operations in the genotype. We generalize this transformation based on a problem-independent parameter that enables a further simplification of the exact phenotype by coarse-graining to produce approximate phenotypes. The concept of these phenotypes (exact and approximate) allows us to clarify what evolved solutions truly predict, making GP models considered at the phenotypic level much better interpretable.
]]></description>
<dc:subject>genetic-programming define-your-terms genotype-phenotype philosophy-of-engineering to-cite hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:79a003e10d49/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:define-your-terms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genotype-phenotype"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-cite"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2107.08013">
    <title>[2107.08013] Machine learning of Kondo physics using variational autoencoders and symbolic regression</title>
    <dc:date>2024-07-10T13:41:50+00:00</dc:date>
    <link>https://arxiv.org/abs/2107.08013</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions. Autoencoders are trained to find a low-dimensional, latent space representation that faithfully characterizes each element of the training set, as measured by a reconstruction error. Variational autoencoders, a probabilistic generalization of standard autoencoders, further condition the learned latent space to promote highly interpretable features. In our study, we find that the learned latent variables strongly correlate with well known, but nontrivial, parameters that characterize emergent behaviors in the Anderson impurity model. In particular, one latent variable correlates with particle-hole asymmetry, while another is in near one-to-one correspondence with the Kondo temperature, a dynamically generated low-energy scale in the impurity model. Using symbolic regression, we model this variable as a function of the known bare physical input parameters and "rediscover" the non-perturbative formula for the Kondo temperature. The machine learning pipeline we develop suggests a general purpose approach which opens opportunities to discover new domain knowledge in other physical systems.
]]></description>
<dc:subject>materials-science learning-from-data genetic-programming symbolic-regression machine-learning rather-interesting clustering pattern-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:af6a41ae0e59/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:materials-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:clustering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pattern-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2405.10267">
    <title>[2405.10267] Sharpness-Aware Minimization in Genetic Programming</title>
    <dc:date>2024-07-07T19:51:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2405.10267</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Sharpness-Aware Minimization (SAM) was recently introduced as a regularization procedure for training deep neural networks. It simultaneously minimizes the fitness (or loss) function and the so-called fitness sharpness. The latter serves as a measure of the nonlinear behavior of a solution and does so by finding solutions that lie in neighborhoods having uniformly similar loss values across all fitness cases. In this contribution, we adapt SAM for tree Genetic Programming (TGP) by exploring the semantic neighborhoods of solutions using two simple approaches. By capitalizing upon perturbing input and output of program trees, sharpness can be estimated and used as a second optimization criterion during the evolution. To better understand the impact of this variant of SAM on TGP, we collect numerous indicators of the evolutionary process, including generalization ability, complexity, diversity, and a recently proposed genotype-phenotype mapping to study the amount of redundancy in trees. The experimental results demonstrate that using any of the two proposed SAM adaptations in TGP allows (i) a significant reduction of tree sizes in the population and (ii) a decrease in redundancy of the trees. When assessed on real-world benchmarks, the generalization ability of the elite solutions does not deteriorate.
]]></description>
<dc:subject>genetic-programming symbolic-regression hey-I-know-this-guy have-read have-reviewed to-write-about to-replicate consider:stochastic-resonance</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:425a6b89476e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-reviewed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-replicate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:stochastic-resonance"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2305.16956">
    <title>[2305.16956] Local Search, Semantics, and Genetic Programming: a Global Analysis</title>
    <dc:date>2024-07-06T19:46:04+00:00</dc:date>
    <link>https://arxiv.org/abs/2305.16956</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Geometric Semantic Geometric Programming (GSGP) is one of the most prominent Genetic Programming (GP) variants, thanks to its solid theoretical background, the excellent performance achieved, and the execution time significantly smaller than standard syntax-based GP. In recent years, a new mutation operator, Geometric Semantic Mutation with Local Search (GSM-LS), has been proposed to include a local search step in the mutation process based on the idea that performing a linear regression during the mutation can allow for a faster convergence to good-quality solutions. While GSM-LS helps the convergence of the evolutionary search, it is prone to overfitting. Thus, it was suggested to use GSM-LS only for a limited number of generations and, subsequently, to switch back to standard geometric semantic mutation. A more recently defined variant of GSGP (called GSGP-reg) also includes a local search step but shares similar strengths and weaknesses with GSM-LS. Here we explore multiple possibilities to limit the overfitting of GSM-LS and GSGP-reg, ranging from adaptive methods to estimate the risk of overfitting at each mutation to a simple regularized regression. The results show that the method used to limit overfitting is not that important: providing that a technique to control overfitting is used, it is possible to consistently outperform standard GSGP on both training and unseen data. The obtained results allow practitioners to better understand the role of local search in GSGP and demonstrate that simple regularization strategies are effective in controlling overfitting.
]]></description>
<dc:subject>semantic-GP genetic-programming hey-I-know-this-guy to-cite consider:compression consider:operational-semantics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c1c05ffd835c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:semantic-GP"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-cite"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:compression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:operational-semantics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2107.09458">
    <title>[2107.09458] Using Shape Constraints for Improving Symbolic Regression Models</title>
    <dc:date>2024-07-05T18:30:21+00:00</dc:date>
    <link>https://arxiv.org/abs/2107.09458</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function. This is relevant in many areas of engineering -- in particular whenever a data-driven model obtained from measurements must have certain properties (e.g. positivity, monotonicity or convexity/concavity). We implement shape constraints using a soft-penalty approach which uses multi-objective algorithms to minimize constraint violations and training error. We use the non-dominated sorting genetic algorithm (NSGA-II) as well as the multi-objective evolutionary algorithm based on decomposition (MOEA/D). We use a set of models from physics textbooks to test the algorithms and compare against earlier results with single-objective algorithms. The results show that all algorithms are able to find models which conform to all shape constraints. Using shape constraints helps to improve extrapolation behavior of the models.
]]></description>
<dc:subject>symbolic-regression hey-I-know-this-guy genetic-programming multiobjective-optimization rather-interesting to-cite to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b900ab1ff938/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-cite"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2202.04708">
    <title>[2202.04708] Active Learning Improves Performance on Symbolic RegressionTasks in StackGP</title>
    <dc:date>2024-07-01T20:28:35+00:00</dc:date>
    <link>https://arxiv.org/abs/2202.04708</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper we introduce an active learning method for symbolic regression using StackGP. The approach begins with a small number of data points for StackGP to model. To improve the model the system incrementally adds a data point such that the new point maximizes prediction uncertainty as measured by the model ensemble. Symbolic regression is re-run with the larger data set. This cycle continues until the system satisfies a termination criterion. We use the Feynman AI benchmark set of equations to examine the ability of our method to find appropriate models using fewer data points. The approach was found to successfully rediscover 72 of the 100 Feynman equations using as few data points as possible, and without use of domain expertise or data translation.
]]></description>
<dc:subject>symbolic-regression hey-I-know-this-guy genetic-programming horse-races stack-based-representations active-learning machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:971305a6cbcd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stack-based-representations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:active-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2304.07089">
    <title>[2304.07089] Analyzing the Interaction Between Down-Sampling and Selection</title>
    <dc:date>2023-08-13T11:12:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2304.07089</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Genetic programming systems often use large training sets to evaluate the quality of candidate solutions for selection. However, evaluating populations on large training sets can be computationally expensive. Down-sampling training sets has long been used to decrease the computational cost of evaluation in a wide range of application domains. Indeed, recent studies have shown that both random and informed down-sampling can substantially improve problem-solving success for GP systems that use the lexicase parent selection algorithm. We use the PushGP framework to experimentally test whether these down-sampling techniques can also improve problem-solving success in the context of two other commonly used selection methods, fitness-proportionate and tournament selection, across eight GP problems (four program synthesis and four symbolic regression). We verified that down-sampling can benefit the problem-solving success of both fitness-proportionate and tournament selection. However, the number of problems wherein down-sampling improved problem-solving success varied by selection scheme, suggesting that the impact of down-sampling depends both on the problem and choice of selection scheme. Surprisingly, we found that down-sampling was most consistently beneficial when combined with lexicase selection as compared to tournament and fitness-proportionate selection. Overall, our results suggest that down-sampling should be considered more often when solving test-based GP problems.
]]></description>
<dc:subject>lexicase genetic-programming sampling metaheuristics performance-measure hey-I-know-this-guy to-write-about consider:counting-solutions consider:landscape consider:multiobjective-tradeoffs</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:70902eeeb474/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lexicase"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sampling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:counting-solutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:landscape"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:multiobjective-tradeoffs"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2203.00528">
    <title>[2203.00528] On genetic programming representations and fitness functions for interpretable dimensionality reduction</title>
    <dc:date>2022-10-04T10:52:32+00:00</dc:date>
    <link>https://arxiv.org/abs/2203.00528</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Dimensionality reduction (DR) is an important technique for data exploration and knowledge discovery. However, most of the main DR methods are either linear (e.g., PCA), do not provide an explicit mapping between the original data and its lower-dimensional representation (e.g., MDS, t-SNE, isomap), or produce mappings that cannot be easily interpreted (e.g., kernel PCA, neural-based autoencoder). Recently, genetic programming (GP) has been used to evolve interpretable DR mappings in the form of symbolic expressions. There exists a number of ways in which GP can be used to this end and no study exists that performs a comparison. In this paper, we fill this gap by comparing existing GP methods as well as devising new ones. We evaluate our methods on several benchmark datasets based on predictive accuracy and on how well the original features can be reconstructed using the lower-dimensional representation only. Finally, we qualitatively assess the resulting expressions and their complexity. We find that various GP methods can be competitive with state-of-the-art DR algorithms and that they have the potential to produce interpretable DR mappings.
]]></description>
<dc:subject>symbolic-regression genetic-programming dimension-reduction numerical-methods no-mention-of-Kotanchek le-sigh</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:054bc1003978/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dimension-reduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:no-mention-of-Kotanchek"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:le-sigh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.09492">
    <title>[2105.09492] DeepCAD: A Deep Generative Network for Computer-Aided Design Models</title>
    <dc:date>2022-03-17T10:50:36+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.09492</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation --- describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic.
]]></description>
<dc:subject>generative-models generative-art representation 3d genetic-programming would-be-simpler to-write-about to-reproduce consider:DSL consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8188b6120ae9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-art"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:3d"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:would-be-simpler"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-reproduce"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:DSL"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-measures"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.01355">
    <title>[2102.01355] Mining Feature Relationships in Data</title>
    <dc:date>2022-03-09T17:31:49+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.01355</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[When faced with a new dataset, most practitioners begin by performing exploratory data analysis to discover interesting patterns and characteristics within data. Techniques such as association rule mining are commonly applied to uncover relationships between features (attributes) of the data. However, association rules are primarily designed for use on binary or categorical data, due to their use of rule-based machine learning. A large proportion of real-world data is continuous in nature, and discretisation of such data leads to inaccurate and less informative association rules. In this paper, we propose an alternative approach called feature relationship mining (FRM), which uses a genetic programming approach to automatically discover symbolic relationships between continuous or categorical features in data. To the best of our knowledge, our proposed approach is the first such symbolic approach with the goal of explicitly discovering relationships between features. Empirical testing on a variety of real-world datasets shows the proposed method is able to find high-quality, simple feature relationships which can be easily interpreted and which provide clear and non-trivial insight into data.
]]></description>
<dc:subject>genetic-programming symbolic-regression feature-selection wheels-reinvented consider:Pareto-GP to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:26e808bec983/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wheels-reinvented"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:Pareto-GP"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.05784">
    <title>[2106.05784] Programming Puzzles</title>
    <dc:date>2022-01-30T11:49:07+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.05784</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce a new type of programming challenge called programming puzzles, as an objective and comprehensive evaluation of program synthesis, and release an open-source dataset of Python Programming Puzzles (P3). Each puzzle is defined by a short Python program f, and the goal is to find an input which makes f return True. The puzzles are objective in that each one is specified entirely by the source code of its verifier f, so evaluating f is all that is needed to test a candidate solution. They do not require an answer key or input/output examples, nor do they depend on natural language understanding. The dataset is comprehensive in that it spans problems of a range of difficulties and domains, ranging from trivial string manipulation problems, to classic programming puzzles (e.g., Tower of Hanoi), to interview/competitive-programming problems (e.g., dynamic programming), to longstanding open problems in algorithms and mathematics (e.g., factoring). We develop baseline enumerative program synthesis, GPT-3 and Codex solvers that are capable of solving puzzles -- even without access to any reference solutions -- by learning from their own past solutions. Codex performs best, solving up to 18% of 397 test problems with a single try and 80% of the problems with 1,000 tries per problem. In a small user study, we find a positive correlation between puzzle-solving performance and coding experience, and between the puzzle difficulty for humans and AI solvers. Therefore, further improvements on P3 could have a significant impact on many program synthesis areas.
]]></description>
<dc:subject>program-synthesis benchmarking genetic-programming machine-learning rather-interesting reinvented-wheels see:PSB-training-sets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:68d17d4b1b00/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:program-synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:benchmarking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinvented-wheels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:see:PSB-training-sets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1810.04735">
    <title>[1810.04735] Towards the Targeted Environment-Specific Evolution of Robot Components</title>
    <dc:date>2021-11-04T10:28:05+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.04735</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which is a significant problem in the field of Evolutionary Robotics. Inspired by the fields of evolutionary art and sculpture, we evolve only targeted parts of a robot, which simplifies the optimisation problem compared to traditional approaches that must simultaneously evolve both (actuated) body and brain. Exploration fidelity is emphasised in areas of the robot most likely to benefit from shape optimisation, whilst exploiting existing robot structure and control. Our approach uses a Genetic Algorithm to optimise collections of Bezier splines that together define the shape of a legged robot's tibia, and leg performance is evaluated in parallel in a high-fidelity simulator. The leg is represented in the simulator as 3D-printable file, and as such can be readily instantiated in reality. Provisional experiments in three distinct environments show the evolution of environment-specific leg structures that are both high-performing and notably different to those evolved in the other environments. This proof-of-concept represents an important step towards the environment-dependent optimisation of performance-critical components for a range of ubiquitous, standard, and already-capable robots that can carry out a wide variety of tasks.
]]></description>
<dc:subject>artificial-life genetic-programming robotics rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:721f870d8405/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:robotics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.11831">
    <title>[2010.11831] Data-driven RANS closures for three-dimensional flows around bluff bodies</title>
    <dc:date>2021-10-22T10:37:33+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.11831</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this short note we apply the recently proposed data-driven RANS closure modelling framework of Schmelzer et al. (2020) to fully three-dimensional, high Reynolds number flows: namely wall-mounted cubes and cuboids at Re=40,000, and a cylinder at Re=140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based on LES or DES reference data. This new model is implemented in a CFD solver, and subsequently applied to prediction of the other flows. We see consistent improvements compared to the baseline k−ω SST model in predictions of mean-velocity in the complete flow domain.
]]></description>
<dc:subject>symbolic-regression genetic-programming learning-from-data fluid-dynamics rather-interesting nonlinear-dynamics to-write-about consider:representation consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:19ae5ae3a8af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fluid-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.08381">
    <title>[2006.08381] DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning</title>
    <dc:date>2021-07-22T09:48:39+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.08381</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Expert problem-solving is driven by powerful languages for thinking about problems and their solutions. Acquiring expertise means learning these languages -- systems of concepts, alongside the skills to use them. We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages. A ``wake-sleep'' learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems. DreamCoder solves both classic inductive programming tasks and creative tasks such as drawing pictures and building scenes. It rediscovers the basics of modern functional programming, vector algebra and classical physics, including Newton's and Coulomb's laws. Concepts are built compositionally from those learned earlier, yielding multi-layered symbolic representations that are interpretable and transferrable to new tasks, while still growing scalably and flexibly with experience.
]]></description>
<dc:subject>genetic-programming software-synthesis machine-learning rather-interesting to-read to-discuss via:lspector</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:48665b88ed61/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software-synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-discuss"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:lspector"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.01238">
    <title>[2010.01238] A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations</title>
    <dc:date>2021-06-27T12:08:53+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.01238</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Art Media Classification problem is a current research area that has attracted attention due to the complex extraction and analysis of features of high-value art pieces. The perception of the attributes can not be subjective, as humans sometimes follow a biased interpretation of artworks while ensuring automated observation's trustworthiness. Machine Learning has outperformed many areas through its learning process of artificial feature extraction from images instead of designing handcrafted feature detectors. However, a major concern related to its reliability has brought attention because, with small perturbations made intentionally in the input image (adversarial attack), its prediction can be completely changed. In this manner, we foresee two ways of approaching the situation: (1) solve the problem of adversarial attacks in current neural networks methodologies, or (2) propose a different approach that can challenge deep learning without the effects of adversarial attacks. The first one has not been solved yet, and adversarial attacks have become even more complex to defend. Therefore, this work presents a Deep Genetic Programming method, called Brain Programming, that competes with deep learning and studies the transferability of adversarial attacks using two artworks databases made by art experts. The results show that the Brain Programming method preserves its performance in comparison with AlexNet, making it robust to these perturbations and competing to the performance of Deep Learning.
]]></description>
<dc:subject>genetic-programming image-processing rather-interesting deep-learning adversarial-methods security to-write-about consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:48fb04d3d3e7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:adversarial-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:security"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.09444">
    <title>[2012.09444] Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning</title>
    <dc:date>2021-05-09T11:44:42+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.09444</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this paper develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To learn the best common and task-specific trees, a new evolutionary process and new fitness functions are developed. The performance of the proposed approach is examined on six multitask problems of 12 image classification datasets with limited training data and compared with three GP and 14 non-GP-based competitive methods. Experimental results show that the new approach outperforms these compared methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability.
]]></description>
<dc:subject>machine-learning genetic-programming multi-task-learning image-processing image-segmentation representation rather-interesting to-try</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a2acce57a15f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multi-task-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-segmentation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-try"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2102.00476">
    <title>[2102.00476] An investigation into the application of genetic programming to combinatorial game theory</title>
    <dc:date>2021-05-09T11:37:27+00:00</dc:date>
    <link>https://arxiv.org/abs/2102.00476</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Genetic programming is the practice of evolving formulas using crossover and mutation of genes representing functional operations. Motivated by genetic evolution we develop and solve two combinatorial games, and we demonstrate some advantages and pitfalls of using genetic programming to investigate Grundy values. We conclude by investigating a combinatorial game whose ruleset and starting positions are inspired by genetic structures.
]]></description>
<dc:subject>genetic-programming game-theory not-my-favorite-representation-scheme to-simulate to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9b869756e018/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:not-my-favorite-representation-scheme"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.09229">
    <title>[2012.09229] Tag-based Genetic Regulation for Genetic Programming</title>
    <dc:date>2021-03-27T11:08:03+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.09229</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce and experimentally demonstrate tag-based genetic regulation, a new genetic programming (GP) technique that allows evolving programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible mechanism for referring to code modules. Tag-based genetic regulation extends existing tag-based naming schemes to allow programs to "promote" and "repress" code modules. This extension allows evolution to structure a program as a gene regulatory network where program modules are regulated based on instruction executions. We demonstrate the functionality of tag-based regulation on a range of program synthesis problems. We find that tag-based regulation improves problem-solving performance on context-dependent problems; that is, problems where programs must adjust how they respond to current inputs based on prior inputs (i.e., current context). We also observe that our implementation of tag-based genetic regulation can impede adaptive evolution when expected outputs are not context-dependent (i.e., the correct response to a particular input remains static over time). Tag-based genetic regulation broadens our repertoire of techniques for evolving more dynamic genetic programs and can easily be incorporated into existing tag-enabled GP systems.
]]></description>
<dc:subject>genetic-programming distributed-processing hey-I-know-this-guy dynamical-systems architecture to-write-about consider:ReQ-similarity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1383bae9e8e8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:distributed-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:ReQ-similarity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2009.12401">
    <title>[2009.12401] Semantic-based Distance Approaches in Multi-objective Genetic Programming</title>
    <dc:date>2021-03-12T15:11:02+00:00</dc:date>
    <link>https://arxiv.org/abs/2009.12401</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Semantics in the context of Genetic Program (GP) can be understood as the behaviour of a program given a set of inputs and has been well documented in improving performance of GP for a range of diverse problems. There have been a wide variety of different methods which have incorporated semantics into single-objective GP. The study of semantics in Multi-objective (MO) GP, however, has been limited and this paper aims at tackling this issue. More specifically, we conduct a comparison of three different forms of semantics in MOGP. One semantic-based method, (i) Semantic Similarity-based Crossover (SSC), is borrowed from single-objective GP, where the method has consistently being reported beneficial in evolutionary search. We also study two other methods, dubbed (ii) Semantic-based Distance as an additional criteriOn (SDO) and (iii) Pivot Similarity SDO. We empirically and consistently show how by naturally handling semantic distance as an additional criterion to be optimised in MOGP leads to better performance when compared to canonical methods and SSC. Both semantic distance based approaches made use of a pivot, which is a reference point from the sparsest region of the search space and it was found that individuals which were both semantically similar and dissimilar to this pivot were beneficial in promoting diversity. Moreover, we also show how the semantics successfully promoted in single-objective optimisation does not necessary lead to a better performance when adopted in MOGP.
]]></description>
<dc:subject>genetic-programming multiobjective-optimization crowding-algorithms algorithms selection-operators-again OK-fine</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7cbb9807729e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crowding-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:selection-operators-again"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:OK-fine"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.06661">
    <title>[2011.06661] Stabilization of the fluidic pinball with gradient-enriched machine learning control</title>
    <dc:date>2021-02-02T21:29:12+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.06661</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We stabilize the flow past a cluster of three rotating cylinders, the fluidic pinball, with automated gradient-enriched machine learning algorithms. The control laws command the rotation speed of each cylinder in an open- and closed-loop manner. These laws are optimized with respect to the average distance from the target steady solution in three successively richer search spaces. First, stabilization is pursued with steady symmetric forcing. Second, we allow for asymmetric steady forcing. And third, we determine an optimal feedback controller employing nine velocity probes downstream. As expected, the control performance increases with every generalization of the search space. Surprisingly, both open- and closed-loop optimal controllers include an asymmetric forcing, which surpasses symmetric forcing. Intriguingly, the best performance is achieved by a combination of phasor control and asymmetric steady forcing. We hypothesize that asymmetric forcing is typical for pitchfork bifurcated dynamics of nominally symmetric configurations. Key enablers are automated machine learning algorithms augmented with gradient search: explorative gradient method for the open-loop parameter optimization and a gradient-enriched machine learning control (gMLC) for the feedback optimization. gMLC learns the control law significantly faster than previously employed genetic programming control.
]]></description>
<dc:subject>control-theory machine-learning rather-interesting dynamical-systems optimization to-simulate to-understand consider:interactive genetic-programming fluid-dynamics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:470579b43f07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:interactive"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fluid-dynamics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www0.cs.ucl.ac.uk/staff/wlangdon/ftp/public/papers/langdon_2020_cec.pdf">
    <title>[PDF] Genetic Improvement of Genetic Programming</title>
    <dc:date>2020-05-02T15:40:45+00:00</dc:date>
    <link>http://www0.cs.ucl.ac.uk/staff/wlangdon/ftp/public/papers/langdon_2020_cec.pdf</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Abstract—GISMOE BNF grammar based GI is applied to optimise run time of the tree interpreter in the fastest single computer floating point genetic programming system, GPavx. Up to two fold speed up is obtained. Performance varies with tree size. The GI version of Singleton’s C++ GPquick is demonstrated on random trees of up to 79 million opcodes on Intel AVX512 SIMD parallel compute servers.]]></description>
<dc:subject>genetic-programming meta-optimization hey-I-know-this-guy to-write-about to-simulate consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:69ce638bf838/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/erp12/CodeBuildingGeneticProgramming-ProtoType">
    <title>erp12/CodeBuildingGeneticProgramming-ProtoType</title>
    <dc:date>2020-05-02T12:46:31+00:00</dc:date>
    <link>https://github.com/erp12/CodeBuildingGeneticProgramming-ProtoType</link>
    <dc:creator>Vaguery</dc:creator><dc:subject>genetic-programming software-synthesis python to-try hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:83a6949ea61a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software-synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-try"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1903.12074">
    <title>[1903.12074] Interpretation of machine learning predictions for patient outcomes in electronic health records</title>
    <dc:date>2020-05-02T11:46:04+00:00</dc:date>
    <link>https://arxiv.org/abs/1903.12074</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions. In this paper we report an empirical study of predictive modeling of several patient outcomes using three state-of-the-art machine learning methods. Our primary goal is to validate the models by interpreting the importance of predictors in the final models. Central to interpretation is the use of feature importance scores, which vary depending on the underlying methodology. In order to assess feature importance, we compared univariate statistical tests, information-theoretic measures, permutation testing, and normalized coefficients from multivariate logistic regression models. In general we found poor correlation between methods in their assessment of feature importance, even when their performance is comparable and relatively good. However, permutation tests applied to random forest and gradient boosting models showed the most agreement, and the importance scores matched the clinical interpretation most frequently.
]]></description>
<dc:subject>machine-learning medical-technology hey-I-know-this-guy genetic-programming healthcare forecasting rather-interesting to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:acb1cc92778d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:medical-technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:healthcare"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:forecasting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.semanticscholar.org/paper/Extending-the-%E2%80%9COpen-Closed-Principle%E2%80%9D-to-Automated-Swan-Adri%C3%A6nsen/08cfc76bf1e3dda2d1004bea6f5c15491d6e3ceb">
    <title>Extending the “Open-Closed Principle” to Automated Algorithm Configuration | Semantic Scholar</title>
    <dc:date>2020-01-19T02:06:58+00:00</dc:date>
    <link>https://www.semanticscholar.org/paper/Extending-the-%E2%80%9COpen-Closed-Principle%E2%80%9D-to-Automated-Swan-Adri%C3%A6nsen/08cfc76bf1e3dda2d1004bea6f5c15491d6e3ceb</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Metaheuristics are an effective and diverse class of optimization algorithms: a means of obtaining solutions of acceptable quality for otherwise intractable problems. The selection, construction, and configuration of a metaheuristic for a given problem has historically been a manually intensive process based on experience, experimentation, and reasoning by metaphor. More recently, there has been interest in automating the process of algorithm configuration. In this article, we identify shared state as an inhibitor of progress for such automation. To solve this problem, we introduce the Automated Open-Closed Principle (AOCP), which stipulates design requirements for unintrusive reuse of algorithm frameworks and automated assembly of algorithms from an extensible palette of components. We demonstrate how the AOCP enables a greater degree of automation than previously possible via an example implementation]]></description>
<dc:subject>hey-I-know-this-guy functional-programming genetic-programming machine-learning to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7b52efefe5f0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:functional-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.semanticscholar.org/paper/Stochastic-synthesis-of-recursive-functions-made-Swan-Krawiec/4ead7bd929db3cb7f0f42bba77168ce31db0b7b9">
    <title>Stochastic synthesis of recursive functions made easy with bananas, lenses, envelopes and barbed wire | Semantic Scholar</title>
    <dc:date>2020-01-19T02:02:44+00:00</dc:date>
    <link>https://www.semanticscholar.org/paper/Stochastic-synthesis-of-recursive-functions-made-Swan-Krawiec/4ead7bd929db3cb7f0f42bba77168ce31db0b7b9</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Stochastic synthesis of recursive functions has historically proved difficult, not least due to issues of non-termination and the often ad hoc methods for addressing this. This article presents a general method of implicit recursion which operates via an automatically-derivable decomposition of datatype structure by cases, thereby ensuring well-foundedness. The method is applied to recursive functions of long-standing interest and the results outperform recent work which combines two leading approaches and employs ‘human in the loop’ to define the recursion structure. We show that stochastic synthesis with the proposed method on benchmark functions is effective even with random search, motivating a need for more difficult recursive benchmarks in future]]></description>
<dc:subject>hey-I-know-this-guy genetic-programming programming-language formal-languages machine-learning to-write-about to-simulate functional-programming</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:57e3141d95c5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming-language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:formal-languages"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:functional-programming"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1905.05264">
    <title>[1905.05264] Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow</title>
    <dc:date>2020-01-14T21:38:07+00:00</dc:date>
    <link>https://arxiv.org/abs/1905.05264</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library TensorFlow to design multi-level quantum gates including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multi-modal fiber. We show that trainable operators at the input and the readout enable one to realize multi-level gates. We study various qudit gates, including the scaling properties of the algorithms with the size of the reservoir. Despite an initial low slop learning stage, TensorFlow turns out to be an extremely versatile resource for designing gates with complex media, including different models that use spatial light modulators with quantized modulation levels.
]]></description>
<dc:subject>quantums quantum-computing machine-learning reinvented-wheels genetic-programming to-write-about snidely</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5aee71263098/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quantums"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quantum-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinvented-wheels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:snidely"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.07848">
    <title>[1906.07848] Symbolic regression by uniform random global search</title>
    <dc:date>2019-12-29T10:21:27+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.07848</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Symbolic regression (SR) is a data analysis problem where we search for the mathematical expression that best fits a numerical dataset. It is a global optimization problem. The most popular approach to SR is by genetic programming (SRGP). It is a common paradigm to compare an algorithm's performance to that of random search, but the data comparing SRGP to random search is lacking. We describe a novel algorithm for SR, namely SR by uniform random global search (SRURGS), also known as pure random search. We conduct experiments comparing SRURGS with SRGP using 100 randomly generated equations. Our results suggest that a SRGP is faster than SRURGS in producing equations with good R^2 for simple problems. However, our experiments suggest that SRURGS is more robust than SRGP, able to produce good output in more challenging problems. As SRURGS is arguably the simplest global search algorithm, we believe it should serve as a control algorithm against which other symbolic regression algorithms are compared. SRURGS has only one tuning parameter, and is conceptually very simple, making it a useful tool in solving SR problems. The method produces random equations, which is useful for the generation of symbolic regression benchmark problems. We have released well documented and open-source python code, currently under formal peer-review, so that interested researchers can deploy the tool in practice.
]]></description>
<dc:subject>genetic-programming symbolic-regression machine-learning algorithms guessing to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0d41c0dac9bc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:guessing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1907.02260">
    <title>[1907.02260] On Explaining Machine Learning Models by Evolving Crucial and Compact Features</title>
    <dc:date>2019-12-29T10:18:03+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.02260</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving non-linear combinations of input features. GP additionally has the potential to improve ML explainability since explicit expressions are evolved. Yet, in most GP works the complexity of evolved features is not explicitly bound or minimized though this is arguably key for explainability. In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm. We consider a simple feature construction scheme using three different GP algorithms, as well as random search, to evolve features for five ML algorithms, including support vector machines and random forest. Our results on 21 datasets pertaining to classification and regression problems show that constructing only two compact features can be sufficient to rival the use of the entire original feature set. We further find that a modern GP algorithm, GP-GOMEA, performs best overall. These results, combined with examples that we provide of readable constructed features and of 2D visualizations of ML behavior, lead us to positively conclude that GP-based feature construction still works well when explicitly searching for compact features, making it extremely helpful to explain ML models.
]]></description>
<dc:subject>reinvented-wheels neural-networks machine-learning bad-bibliography genetic-programming as-if-nothing-had-happened-for-20-years meh</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8ed7c639cdc4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinvented-wheels"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bad-bibliography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:as-if-nothing-had-happened-for-20-years"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.00945">
    <title>[1910.00945] Optimising Optimisers with Push GP</title>
    <dc:date>2019-12-15T13:07:00+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.00945</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This work uses Push GP to automatically design both local and population-based optimisers for continuous-valued problems. The optimisers are trained on a single function optimisation landscape, using random transformations to discourage overfitting. They are then tested for generality on larger versions of the same problem, and on other continuous-valued problems. In most cases, the optimisers generalise well to the larger problems. Surprisingly, some of them also generalise very well to previously unseen problems, outperforming existing general purpose optimisers such as CMA-ES. Analysis of the behaviour of the evolved optimisers indicates a range of interesting optimisation strategies that are not found within conventional optimisers, suggesting that this approach could be useful for discovering novel and effective forms of optimisation in an automated manner.
]]></description>
<dc:subject>Push genetic-programming rather-interesting to-do to-write-about consider:using-recent-improvements consider:ontology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d2125da1a102/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Push"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:using-recent-improvements"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:ontology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1709.05394">
    <title>[1709.05394] A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection</title>
    <dc:date>2019-11-03T11:45:32+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.05394</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has demonstrated the ability of lexicase selection to solve difficult problems in program synthesis and symbolic regression, the central goal of this paper is to develop the theoretical underpinnings that explain its performance. To this end, we derive an analytical formula that gives the expected probabilities of selection under lexicase selection, given a population and its behavior. In addition, we expand upon the relation of lexicase selection to many-objective optimization methods to describe the behavior of lexicase selection, which is to select individuals on the boundaries of Pareto fronts in high-dimensional space. We show analytically why lexicase selection performs more poorly for certain sizes of population and training cases, and show why it has been shown to perform more poorly in continuous error spaces. To address this last concern, we propose new variants of epsilon-lexicase selection, a method that modifies the pass condition in lexicase selection to allow near-elite individuals to pass cases, thereby improving selection performance with continuous errors. We show that epsilon-lexicase outperforms several diversity-maintenance strategies on a number of real-world and synthetic regression problems.
]]></description>
<dc:subject>hey-I-know-this-guy genetic-programming lexicase-selection looking-to-see symbolic-regression horse-races to-simulate to-write-about consider:generalizations consider:multiobjective-cases consider:categorical-work</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9032216806ca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lexicase-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:generalizations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:multiobjective-cases"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:categorical-work"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1608.07617">
    <title>[1608.07617] &quot;Sampling&quot;' as a Baseline Optimizer for Search-based Software Engineering</title>
    <dc:date>2019-08-30T10:59:42+00:00</dc:date>
    <link>https://arxiv.org/abs/1608.07617</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions. We call this method "SWAY", short for "the sampling way". Sway is very simple to implement and, in studies with various software engineering models, this sampling approach was found to be competitive with corresponding state-of-the-art evolutionary algorithms while requiring far less computation cost. Considering the simplicity and effectiveness of Sway, we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute.
]]></description>
<dc:subject>metaheuristics genetic-programming multiobjective-optimization evolutionary-algorithms performance-measure rather-interesting search-operators to-write-about to-replicate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d1ada658a31a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:search-operators"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-replicate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ddd.fit.cvut.cz/prj/Benchmarks/index.php?page=download">
    <title>Collection of Digital Design Benchmarks</title>
    <dc:date>2019-07-29T11:03:27+00:00</dc:date>
    <link>https://ddd.fit.cvut.cz/prj/Benchmarks/index.php?page=download</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Collection of Digital Design Benchmarks]]></description>
<dc:subject>benchmarking engineering-design circuits genetic-programming constraint-satisfaction performance-measure to-write-about boolean-networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:db5180be6a39/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:benchmarking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:circuits"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:boolean-networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ieeexplore.ieee.org/abstract/document/5763326">
    <title>A global postsynthesis optimization method for combinational circuits - IEEE Conference Publication</title>
    <dc:date>2019-06-27T10:57:14+00:00</dc:date>
    <link>https://ieeexplore.ieee.org/abstract/document/5763326</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A genetic programming-based circuit synthesis method is proposed that enables to globally optimize the number of gates in circuits that have already been synthesized using common methods such as ABC and SIS. The main contribution is a proposal for a new fitness function that enables to significantly reduce the fitness evaluation time in comparison to the state of the art. The fitness function performs optimized equivalence checking using a SAT solver. It is shown that the equivalence checking time can significantly be reduced when knowledge of the parent circuit and its mutated offspring is taken into account. For a cost of a runtime, results of conventional synthesis conducted using SIS and ABC were improved by 20-40% for the LGSynth93 benchmarks.
]]></description>
<dc:subject>genetic-programming Cartesian-GP boolean-networks boolean-matching discrete-mathematics rather-interesting algorithms to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:79ad77ac6b0a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Cartesian-GP"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:boolean-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:boolean-matching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:discrete-mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://export.arxiv.org/abs/1904.08658">
    <title>[1904.08658] Batch Tournament Selection for Genetic Programming</title>
    <dc:date>2019-04-23T21:36:39+00:00</dc:date>
    <link>https://export.arxiv.org/abs/1904.08658</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many applications. In this paper, we introduce Batch Tournament Selection (BTS), a hybrid of tournament and lexicase selection which is approximately one order of magnitude faster than lexicase selection while achieving a competitive quality of solutions. Tests on a number of regression datasets show that BTS compares well with lexicase selection in terms of mean absolute error while having a speed-up of up to 25 times. Surprisingly, BTS and lexicase selection have almost no difference in both diversity and performance. This reveals that batches and ordered test cases are completely different mechanisms which share the same general principle fostering the specialization of individuals. This work introduces an efficient algorithm that sheds light onto the main principles behind the success of lexicase, potentially opening up a new range of possibilities for algorithms to come.
]]></description>
<dc:subject>genetic-programming selection algorithms hey-I-know-this-guy (I-wish-it-wasn't-always-a-guy) to-write-about to-do</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b3873cff00ec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:(I-wish-it-wasn't-always-a-guy)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://dl.acm.org/citation.cfm?id=3071266">
    <title>Geometric semantic genetic programming for recursive boolean programs</title>
    <dc:date>2019-04-08T00:59:55+00:00</dc:date>
    <link>https://dl.acm.org/citation.cfm?id=3071266</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Geometric Semantic Genetic Programming (GSGP) induces a unimodal fitness landscape for any problem that consists in finding a function fitting given input/output examples. Most of the work around GSGP to date has focused on real-world applications and on improving the originally proposed search operators, rather than on broadening its theoretical framework to new domains. We extend GSGP to recursive programs, a notoriously challenging domain with highly discontinuous fitness landscapes. We focus on programs that map variable-length Boolean lists to Boolean values, and design search operators that are provably efficient in the training phase and attain perfect generalization. Computational experiments complement the theory and demonstrate the superiority of the new operators to the conventional ones. This work provides new insights into the relations between program syntax and semantics, search operators and fitness landscapes, also for more general recursive domains.]]></description>
<dc:subject>genetic-programming hey-I-know-this-guy generative-programming to-understand to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:797666f76f27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1809.04209">
    <title>[1809.04209] Bidirectional Evaluation with Direct Manipulation</title>
    <dc:date>2019-02-13T11:37:37+00:00</dc:date>
    <link>https://arxiv.org/abs/1809.04209</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present an evaluation update (or simply, update) algorithm for a full-featured functional programming language, which synthesizes program changes based on output changes. Intuitively, the update algorithm retraces the steps of the original evaluation, rewriting the program as needed to reconcile differences between the original and updated output values. Our approach, furthermore, allows expert users to define custom lenses that augment the update algorithm with more advanced or domain-specific program updates. 
To demonstrate the utility of evaluation update, we implement the algorithm in Sketch-n-Sketch, a novel direct manipulation programming system for generating HTML documents. In Sketch-n-Sketch, the user writes an ML-style functional program to generate HTML output. When the user directly manipulates the output using a graphical user interface, the update algorithm reconciles the changes. We evaluate bidirectional evaluation in Sketch-n-Sketch by authoring ten examples comprising approximately 1400 lines of code in total. These examples demonstrate how a variety of HTML documents and applications can be developed and edited interactively in Sketch-n-Sketch, mitigating the tedious edit-run-view cycle in traditional programming environments.
]]></description>
<dc:subject>rather-interesting usability functional-languages computer-science programming-language to-understand inference ReQ genetic-programming consider:GP</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3367e7773c8d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:usability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:functional-languages"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computer-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:programming-language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ReQ"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:GP"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1809.07406">
    <title>[1809.07406] Exploiting Tournament Selection for Efficient Parallel Genetic Programming</title>
    <dc:date>2019-02-10T16:51:55+00:00</dc:date>
    <link>https://arxiv.org/abs/1809.07406</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Genetic Programming (GP) is a computationally intensive technique which is naturally parallel in nature. Consequently, many attempts have been made to improve its run-time from exploiting highly parallel hardware such as GPUs. However, a second methodology of improving the speed of GP is through efficiency techniques such as subtree caching. However achieving parallel performance and efficiency is a difficult task. This paper will demonstrate an efficiency saving for GP compatible with the harnessing of parallel CPU hardware by exploiting tournament selection. Significant efficiency savings are demonstrated whilst retaining the capability of a high performance parallel implementation of GP. Indeed, a 74% improvement in the speed of GP is achieved with a peak rate of 96 billion GPop/s for classification type problems.
]]></description>
<dc:subject>genetic-programming horse-races tournament-selection selection metaheuristics have-done to-write-about consider:deathless-GP-then-what consider:lexicase</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5a312fb9b2dd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tournament-selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-done"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:deathless-GP-then-what"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:lexicase"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://search.proquest.com/openview/495a34127566e9fcd0bdb0772523ccf4/1?pq-origsite=gscholar&amp;cbl=18750&amp;diss=y">
    <title>Prioritized Grammar Enumeration: A novel method for symbolic regression - ProQuest</title>
    <dc:date>2019-02-10T01:31:27+00:00</dc:date>
    <link>https://search.proquest.com/openview/495a34127566e9fcd0bdb0772523ccf4/1?pq-origsite=gscholar&amp;cbl=18750&amp;diss=y</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Prioritized Grammar Enumeration: A novel method for symbolic regression]]></description>
<dc:subject>genetic-programming thesis algorithms to-read archives-and-caches have-done rather-interesting compare-notes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6ffa7934e324/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:thesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:archives-and-caches"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-done"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:compare-notes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1812.05225">
    <title>[1812.05225] Finding the origin of noise transients in LIGO data with machine learning</title>
    <dc:date>2019-01-27T12:35:14+00:00</dc:date>
    <link>https://arxiv.org/abs/1812.05225</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing non-astrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the data sets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.]]></description>
<dc:subject>genetic-programming hey-I-know-this-guy astrophysics data-analysis data-mining to-understand feature-construction classification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:97dd967c5c54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:astrophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-construction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://peerj.com/preprints/27122/">
    <title>What else is in an evolved name? Exploring evolvable specificity with SignalGP [PeerJ Preprints]</title>
    <dc:date>2019-01-26T11:40:02+00:00</dc:date>
    <link>https://peerj.com/preprints/27122/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Tags are evolvable labels that provide genetic programs a flexible mechanism for specification. Tags are used to label and refer to programmatic elements, such as functions or jump targets. However, tags differ from traditional, more rigid methods for handling labeling because they allow for inexact references; that is, a referring tag need not exactly match its referent. Here, we explore how adjusting the threshold for how what qualifies as a match affects adaptive evolution. Further, we propose broadened applications of tags in the context of a genetic programming (GP) technique called SignalGP. SignalGP gives evolution direct access to the event-driven paradigm. Program modules in SignalGP are tagged and can be triggered by signals (with matching tags) from the environment, from other agents, or due to internal regulation. Specifically, we propose to extend this tag based system to: (1) provide more fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical program organization to evolve de novo).

]]></description>
<dc:subject>artificial-life genetic-programming representation hey-I-know-this-guy the-mangle-in-practice to-examine consider:`ReQ`</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b1a1557763cd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-examine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:`ReQ`"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.sciencedirect.com/science/article/pii/S2210650218300208?dgcid=coauthor">
    <title>Alignment-based genetic programming for real life applications - ScienceDirect</title>
    <dc:date>2019-01-19T13:17:52+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S2210650218300208?dgcid=coauthor</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A recent discovery has attracted the attention of many researchers in the field of genetic programming: given individuals with particular characteristics of alignment in the error space, called optimally aligned, it is possible to reconstruct a globally optimal solution. Furthermore, recent preliminary experiments have shown that an indirect search consisting of looking for optimally aligned individuals can have benefits in terms of generalization ability compared to a direct search for optimal solutions. For this reason, defining genetic programming systems that look for optimally aligned individuals is becoming an ambitious and important objective. Nevertheless, the systems that have been introduced so far present important limitations that make them unusable in practice, particularly for complex real-life applications. In this paper, we overcome those limitations, and we present the first usable alignment-based genetic programming system, called nested alignment genetic programming (NAGP). The presented experimental results show that NAGP is able to outperform two of the most recognized state-of-the-art genetic programming systems on four complex real-life applications. The predictive models generated by NAGP are not only more effective than the ones produced by the other studied methods but also significantly smaller and thus more manageable and interpretable.

]]></description>
<dc:subject>symbolic-regression genetic-programming hybrid-methods system-identification have-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7f84383f1550/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hybrid-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1803.09473">
    <title>[1803.09473] code2vec: Learning Distributed Representations of Code</title>
    <dc:date>2018-12-09T12:24:41+00:00</dc:date>
    <link>https://arxiv.org/abs/1803.09473</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present a neural model for representing snippets of code as continuous distributed vectors ("code embeddings"). The main idea is to represent a code snippet as a single fixed-length code vector, which can be used to predict semantic properties of the snippet. This is performed by decomposing code to a collection of paths in its abstract syntax tree, and learning the atomic representation of each path simultaneously with learning how to aggregate a set of them. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 14M methods. We show that code vectors trained on this dataset can predict method names from files that were completely unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. Comparing previous techniques over the same data set, our approach obtains a relative improvement of over 75%, being the first to successfully predict method names based on a large, cross-project, corpus. Our trained model, visualizations and vector similarities are available as an interactive online demo at this http URL. The code, data, and trained models are available at this https URL.
]]></description>
<dc:subject>representation genetic-programming (it-ain't) deep-learning neural-networks feature-construction to-write-about discrete-and-continuous-sittin-in-a-tree</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4d79f4bd1377/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:(it-ain't)"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-construction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:discrete-and-continuous-sittin-in-a-tree"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1186/s40069-018-0300-5">
    <title>Accurate High Performance Concrete Prediction with an Alignment-Based Genetic Programming System | SpringerLink</title>
    <dc:date>2018-11-29T12:46:39+00:00</dc:date>
    <link>https://link.springer.com/article/10.1186/s40069-018-0300-5</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In 2013, our research group published a contribution in which a new version of genetic programming, called Geometric Semantic Genetic Programming (GSGP), was fostered as an appropriate computational intelligence method for predicting the strength of high-performance concrete. That successful work, in which GSGP was shown to outperform the existing systems, allowed us to promote GSGP as the new state-of-the-art technology for high-performance concrete strength prediction. In this paper, we propose, for the first time, a novel genetic programming system called Nested Align Genetic Programming (NAGP). NAGP exploits semantic awareness in a completely different way compared to GSGP. The reported experimental results show that NAGP is able to significantly outperform GSGP for high-performance concrete strength prediction. More specifically, not only NAGP is able to obtain more accurate predictions than GSGP, but NAGP is also able to generate predictive models with a much smaller size, and thus easier to understand and interpret, than the ones generated by GSGP. Thanks to this ability of NAGP, we are able here to show the model evolved by NAGP, which was impossible for GSGP.

]]></description>
<dc:subject>symbolic-regression genetic-programming numerical-methods models algorithms to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f70f2d5249d2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1811.10665">
    <title>[1811.10665] Stepping Stones to Inductive Synthesis of Low-Level Looping Programs</title>
    <dc:date>2018-11-28T23:13:32+00:00</dc:date>
    <link>https://arxiv.org/abs/1811.10665</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Inductive program synthesis, from input/output examples, can provide an opportunity to automatically create programs from scratch without presupposing the algorithmic form of the solution. For induction of general programs with loops (as opposed to loop-free programs, or synthesis for domain-specific languages), the state of the art is at the level of introductory programming assignments. Most problems that require algorithmic subtlety, such as fast sorting, have remained out of reach without the benefit of significant problem-specific background knowledge. A key challenge is to identify cues that are available to guide search towards correct looping programs. We present MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes low-level looping programs from input/output examples. During search, delayed acceptance bypasses small gains to identify significantly-improved stepping stone programs that tend to generalize and enable further progress. The method performs well on a set of established benchmarks, and succeeds on the previously unsolved "Collatz Numbers" program synthesis problem. Additional benchmarks include the problem of rapidly sorting integer arrays, in which we observe the emergence of comb sort (a Shell sort variant that is empirically fast). MAKESPEARE has also synthesized a record-setting program on one of the puzzles from the TIS-100 assembly language programming game.
]]></description>
<dc:subject>benchmarking genetic-programming software-synthesis rather-interesting to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:21286fb740ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:benchmarking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software-synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1810.04756">
    <title>[1810.04756] Stochastic Synthesis for Stochastic Computing</title>
    <dc:date>2018-10-28T11:17:42+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.04756</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Stochastic computing (SC) is an emerging computing technique which offers higher computational density, and lower power over binary-encoded (BE) computation. Unlike BE computation, SC encodes values as probabilistic bitstreams which makes designing new circuits unintuitive. Existing techniques for synthesizing SC circuits are limited to specific classes of functions such as polynomial evaluation or constant scaling. In this paper, we propose using stochastic synthesis, which is originally a program synthesis technique, to automate the task of synthesizing new SC circuits. Our results show stochastic synthesis is more general than past techniques and can synthesize manually designed SC circuits as well as new ones such as an approximate square root unit.]]></description>
<dc:subject>via:jhmoore representation genetic-programming circuits rather-interesting computational-methods to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ac40a5cdc124/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:jhmoore"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:circuits"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1804.05445">
    <title>[1804.05445] Evolving Event-driven Programs with SignalGP</title>
    <dc:date>2018-05-16T12:24:54+00:00</dc:date>
    <link>https://arxiv.org/abs/1804.05445</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present SignalGP, a new genetic programming (GP) technique designed to incorporate the event-driven programming paradigm into computational evolution's toolbox. Event-driven programming is a software design philosophy that simplifies the development of reactive programs by automatically triggering program modules (event-handlers) in response to external events, such as signals from the environment or messages from other programs. SignalGP incorporates these concepts by extending existing tag-based referencing techniques into an event-driven context. Both events and functions are labeled with evolvable tags; when an event occurs, the function with the closest matching tag is triggered. In this work, we apply SignalGP in the context of linear GP. We demonstrate the value of the event-driven paradigm using two distinct test problems (an environment coordination problem and a distributed leader election problem) by comparing SignalGP to variants that are otherwise identical, but must actively use sensors to process events or messages. In each of these problems, rapid interaction with the environment or other agents is critical for maximizing fitness. We also discuss ways in which SignalGP can be generalized beyond our linear GP implementation.
]]></description>
<dc:subject>gptp hey-I-know-this-guy genetic-programming representation to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:65995a346284/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:gptp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1711.07387">
    <title>[1711.07387] How morphological development can guide evolution</title>
    <dc:date>2017-12-03T13:24:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1711.07387</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Organisms result from multiple adaptive processes occurring and interacting at different time scales. One such interaction is that between development and evolution. In modeling studies, it has been shown that development sweeps over a series of traits in a single agent, and sometimes exposes promising static traits. Subsequent evolution can then canalize these rare traits. Thus, development can, under the right conditions, increase evolvability. Here, we report on a previously unknown phenomenon when embodied agents are allowed to develop and evolve: Evolution discovers body plans which are robust to control changes, these body plans become genetically assimilated, yet controllers for these agents are not assimilated. This allows evolution to continue climbing fitness gradients by tinkering with the developmental programs for controllers within these permissive body plans. This exposes a previously unknown detail about the Baldwin effect: instead of all useful traits becoming genetically assimilated, only phenotypic traits that render the agent robust to changes in other traits become assimilated. We refer to this phenomenon as differential canalization. This finding also has important implications for the evolutionary design of artificial and embodied agents such as robots: robots that are robust to internal changes in their controllers may also be robust to external changes in their environment, such as transferal from simulation to reality, or deployment in novel environments.
]]></description>
<dc:subject>artificial-life evolved-devo developmental-biology representation rather-interesting genetic-programming hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d6e01bd87f2e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolved-devo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:developmental-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1709.07417">
    <title>[1709.07417] Neural Optimizer Search with Reinforcement Learning</title>
    <dc:date>2017-09-26T12:08:50+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.07417</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We present an approach to automate the process of discovering optimization methods, with a focus on deep learning architectures. We train a Recurrent Neural Network controller to generate a string in a domain specific language that describes a mathematical update equation based on a list of primitive functions, such as the gradient, running average of the gradient, etc. The controller is trained with Reinforcement Learning to maximize the performance of a model after a few epochs. On CIFAR-10, our method discovers several update rules that are better than many commonly used optimizers, such as Adam, RMSProp, or SGD with and without Momentum on a ConvNet model. We introduce two new optimizers, named PowerSign and AddSign, which we show transfer well and improve training on a variety of different tasks and architectures, including ImageNet classification and Google's neural machine translation system.
]]></description>
<dc:subject>meta-optimization genetic-programming representation to-write-about deep-learning rather-interesting reinventing-the-wheel-again</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:81d70923dc01/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reinventing-the-wheel-again"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1709.05703">
    <title>[1709.05703] AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms</title>
    <dc:date>2017-09-23T17:12:13+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.05703</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we present the first-of-its-kind machine learning (ML) system, called AI Programmer, that can automatically generate full software programs requiring only minimal human guidance. At its core, AI Programmer uses genetic algorithms (GA) coupled with a tightly constrained programming language that minimizes the overhead of its ML search space. Part of AI Programmer's novelty stems from (i) its unique system design, including an embedded, hand-crafted interpreter for efficiency and security and (ii) its augmentation of GAs to include instruction-gene randomization bindings and programming language-specific genome construction and elimination techniques. We provide a detailed examination of AI Programmer's system design, several examples detailing how the system works, and experimental data demonstrating its software generation capabilities and performance using only mainstream CPUs.]]></description>
<dc:subject>erm kinda-sorta-familiar wheel-reinvention genetic-programming system-of-professions to-write-about to-you-know-REALLY-WRITE-ABOUT</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:30d365c79a6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:erm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:kinda-sorta-familiar"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:wheel-reinvention"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-of-professions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-you-know-REALLY-WRITE-ABOUT"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1708.03157">
    <title>[1708.03157] TensorFlow Enabled Genetic Programming</title>
    <dc:date>2017-09-23T12:07:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1708.03157</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Genetic Programming, a kind of evolutionary computation and machine learning algorithm, is shown to benefit significantly from the application of vectorized data and the TensorFlow numerical computation library on both CPU and GPU architectures. The open source, Python Karoo GP is employed for a series of 190 tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data points. This body of tests demonstrates that datasets measured in tens and hundreds of data points see 2-15x improvement when moving from the scalar/SymPy configuration to the vector/TensorFlow configuration, with a single core performing on par or better than multiple CPU cores and GPUs. A dataset composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing 5.5M data points sees GPU configurations out-performing CPU configurations on average by 1.3x.
]]></description>
<dc:subject>hey-I-know-this-guy genetic-programming symbolic-regression library GPU to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:04638089cf01/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:library"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GPU"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1306.5667">
    <title>[1306.5667] Using Genetic Programming to Model Software</title>
    <dc:date>2017-04-30T12:40:00+00:00</dc:date>
    <link>https://arxiv.org/abs/1306.5667</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study a generic program to investigate the scope for automatically customising it for a vital current task, which was not considered when it was first written. In detail, we show genetic programming (GP) can evolve models of aspects of BLAST's output when it is used to map Solexa Next-Gen DNA sequences to the human genome.
]]></description>
<dc:subject>bioinformatics software-synthesis algorithms genetic-programming hey-I-know-this-guy nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5ff9a0ec790f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software-synthesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1702.05624">
    <title>[1702.05624] Reproducing and learning new algebraic operations on word embeddings using genetic programming</title>
    <dc:date>2017-04-28T21:41:25+00:00</dc:date>
    <link>https://arxiv.org/abs/1702.05624</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Word-vector representations associate a high dimensional real-vector to every word from a corpus. Recently, neural-network based methods have been proposed for learning this representation from large corpora. This type of word-to-vector embedding is able to keep, in the learned vector space, some of the syntactic and semantic relationships present in the original word corpus. This, in turn, serves to address different types of language classification tasks by doing algebraic operations defined on the vectors. The general practice is to assume that the semantic relationships between the words can be inferred by the application of a-priori specified algebraic operations. Our general goal in this paper is to show that it is possible to learn methods for word composition in semantic spaces. Instead of expressing the compositional method as an algebraic operation, we will encode it as a program, which can be linear, nonlinear, or involve more intricate expressions. More remarkably, this program will be evolved from a set of initial random programs by means of genetic programming (GP). We show that our method is able to reproduce the same behavior as human-designed algebraic operators. Using a word analogy task as benchmark, we also show that GP-generated programs are able to obtain accuracy values above those produced by the commonly used human-designed rule for algebraic manipulation of word vectors. Finally, we show the robustness of our approach by executing the evolved programs on the word2vec GoogleNews vectors, learned over 3 billion running words, and assessing their accuracy in the same word analogy task.
]]></description>
<dc:subject>genetic-programming natural-language-processing nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2acdecb80fb9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:natural-language-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1611.04766">
    <title>[1611.04766] Differentiable Genetic Programming</title>
    <dc:date>2017-04-24T12:34:22+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.04766</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning. The resulting machine learning framework is called differentiable Cartesian Genetic Programming (dCGP). In the context of symbolic regression, dCGP offers a new approach to the long unsolved problem of constant representation in GP expressions. On several problems of increasing complexity we find that dCGP is able to find the exact form of the symbolic expression as well as the constants values. We also demonstrate the use of dCGP to solve a large class of differential equations and to find prime integrals of dynamical systems, presenting, in both cases, results that confirm the efficacy of our approach.
]]></description>
<dc:subject>genetic-programming algorithms rather-interesting to-write-about very-nice machine-learning symbolic-regression nudge do-this</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c7d80e34b065/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:very-nice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:do-this"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1704.00764">
    <title>[1704.00764] A Genetic Programming Approach to Designing Convolutional Neural Network Architectures</title>
    <dc:date>2017-04-10T09:52:20+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.00764</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.
]]></description>
<dc:subject>genetic-programming neural-networks architecture cartesian-GP rather-interesting engineering-design nudge-targets consider:looking-to-see algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b8333026048a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:architecture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cartesian-GP"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1704.00828">
    <title>[1704.00828] A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems</title>
    <dc:date>2017-04-10T09:50:32+00:00</dc:date>
    <link>https://arxiv.org/abs/1704.00828</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods were proposed to overcome this issue through a probability model that captures the structure of the fit individuals and use it to sample new individuals. This work proposes the use of LGP with a Stochastic Context-Free Grammar (SCFG), that has a probability distribution that is updated according to selected individuals. We proposed a method for adapting the grammar into the linear representation of LGP. Tests performed with the proposed probabilistic method, and with two hybrid approaches, on several symbolic regression benchmark problems show that the results are statistically better than the obtained by the traditional LGP.
]]></description>
<dc:subject>genetic-programming search-operators generative-models looking-to-see rather-interesting algorithms metaheuristics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1c1d503138d6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:search-operators"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:generative-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1701.03641">
    <title>[1701.03641] Symbolic Regression Algorithms with Built-in Linear Regression</title>
    <dc:date>2017-04-04T18:49:25+00:00</dc:date>
    <link>https://arxiv.org/abs/1701.03641</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recently, several algorithms for symbolic regression (SR) emerged which employ a form of multiple linear regression (LR) to produce generalized linear models. The use of LR allows the algorithms to create models with relatively small error right from the beginning of the search; such algorithms are thus claimed to be (sometimes by orders of magnitude) faster than SR algorithms based on vanilla genetic programming. However, a systematic comparison of these algorithms on a common set of problems is still missing. In this paper we conceptually and experimentally compare several representatives of such algorithms (GPTIPS, FFX, and EFS). They are applied as off-the-shelf, ready-to-use techniques, mostly using their default settings. The methods are compared on several synthetic and real-world SR benchmark problems. Their performance is also related to the performance of three conventional machine learning algorithms --- multiple regression, random forests and support vector regression.
]]></description>
<dc:subject>genetic-programming symbolic-regression horse-races consider:reporting-complexity</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7b5c2171ac46/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:reporting-complexity"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1611.01120">
    <title>[1611.01120] Generating Families of Practical Fast Matrix Multiplication Algorithms</title>
    <dc:date>2016-12-31T13:09:11+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.01120</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Matrix multiplication (GEMM) is a core operation to numerous scientific applications. Traditional implementations of Strassen-like fast matrix multiplication (FMM) algorithms often do not perform well except for very large matrix sizes, due to the increased cost of memory movement, which is particularly noticeable for non-square matrices. Such implementations also require considerable workspace and modifications to the standard BLAS interface. We propose a code generator framework to automatically implement a large family of FMM algorithms suitable for multiplications of arbitrary matrix sizes and shapes. By representing FMM with a triple of matrices [U,V,W] that capture the linear combinations of submatrices that are formed, we can use the Kronecker product to define a multi-level representation of Strassen-like algorithms. Incorporating the matrix additions that must be performed for Strassen-like algorithms into the inherent packing and micro-kernel operations inside GEMM avoids extra workspace and reduces the cost of memory movement. Adopting the same loop structures as high-performance GEMM implementations allows parallelization of all FMM algorithms with simple but efficient data parallelism without the overhead of task parallelism. We present a simple performance model for general FMM algorithms and compare actual performance of 20+ FMM algorithms to modeled predictions. Our implementations demonstrate a performance benefit over conventional GEMM on single core and multi-core systems. This study shows that Strassen-like fast matrix multiplication can be incorporated into libraries for practical use.
]]></description>
<dc:subject>genetic-programming rather-interesting matrices algorithms representation nudge-targets consider:looking-to-see consider:comparing-theory-and-practice</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:17b27978d74c/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:matrices"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:comparing-theory-and-practice"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1605.06640">
    <title>[1605.06640] Programming with a Differentiable Forth Interpreter</title>
    <dc:date>2016-05-28T21:05:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1605.06640</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[There are families of neural networks that can learn to compute any function, provided sufficient training data. However, given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. Here we consider the case of prior procedural knowledge such as knowing the overall recursive structure of a sequence transduction program or the fact that a program will likely use arithmetic operations on real numbers to solve a task. To this end we present a differentiable interpreter for the programming language Forth. Through a neural implementation of the dual stack machine that underlies Forth, programmers can write program sketches with slots that can be filled with learnable behaviour. As the program interpreter is end-to-end differentiable, we can optimize this behaviour directly through gradient descent techniques on user specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex transduction tasks such as sequence sorting or addition with substantially less data and better generalisation over problem sizes. In addition, we introduce neural program optimisations based on symbolic computation and parallel branching that lead to significant speed improvements.
]]></description>
<dc:subject>neural-networks genetic-programming weird-ass-hybrids metaheuristics nudge-targets consider:looking-to-see to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:61f60e905076/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:weird-ass-hybrids"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1605.01514">
    <title>[1605.01514] Fitness-based Adaptive Control of Parameters in Genetic Programming: Adaptive Value Setting of Mutation Rate and Flood Mechanisms</title>
    <dc:date>2016-05-09T10:58:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1605.01514</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper concerns applications of genetic algorithms and genetic programming to tasks for which it is difficult to find a representation that does not map to a highly complex and discontinuous fitness landscape. In such cases the standard algorithm is prone to getting trapped in local extremes. The paper proposes several adaptive mechanisms that are useful in preventing the search from getting trapped.
]]></description>
<dc:subject>genetic-programming 1995-called meh</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9b482999f3bb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:1995-called"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meh"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://dl.acm.org/citation.cfm?id=1389336">
    <title>Memory with memory</title>
    <dc:date>2015-06-22T20:32:48+00:00</dc:date>
    <link>http://dl.acm.org/citation.cfm?id=1389336</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Based in part on observations about the incremental nature of most state changes in biological systems, we introduce the idea of Memory with Memory in Genetic Programming (GP), where we use "soft" assignments to registers instead of the "hard" assignments used in most computer science (including traditional GP). Instead of having the new value completely overwrite the old value of the register, these soft assignments combine the old and new values.

We then report on extensive empirical tests (a total of 12,800 runs) on symbolic regression problems where Memory with Memory GP almost always does as well as traditional GP, while significantly outperforming it in several cases. Memory with Memory GP also tends to be far more consistent, having much less variation in its best-of-run fitnesses than traditional GP. The data suggest that Memory with Memory GP works by successively refining an approximate solution to the target problem. This means it can continue to improve (if slowly) over time, but that it is less likely to get the sort of exact solution that one might find with traditional GP. The use of soft assignment also means that Memory with Memory GP is much less likely to have truly ineffective code, but the action of successive refinement of approximations means that the average program size is often larger than with traditional GP.]]></description>
<dc:subject>genetic-programming the-mangle-in-practice contingency simulation rather-interesting hey-I-know-this-guy x-2 nudge-targets consider:do-it-with-Push from-the-horse's-mouth in-light-of-West-Eberhard-book</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5b23eb978b89/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:contingency"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:x-2"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:do-it-with-Push"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:from-the-horse's-mouth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:in-light-of-West-Eberhard-book"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>