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    <description>recent bookmarks from Vaguery</description>
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    <title>[2504.11440] Greedy Restart Schedules: A Baseline for Dynamic Algorithm Selection on Numerical Black-box Optimization Problems</title>
    <dc:date>2026-02-20T14:04:43+00:00</dc:date>
    <link>https://arxiv.org/abs/2504.11440</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In many optimization domains, there are multiple different solvers that contribute to the overall state-of-the-art, each performing better on some, and worse on other types of problem instances. Meta-algorithmic approaches, such as instance-based algorithm selection, configuration and scheduling, aim to close this gap by extracting the most performance possible from a set of (configurable) optimizers. In this context, the best performing individual algorithms are often hand-crafted hybrid heuristics which perform many restarts of fast local optimization approaches. However, data-driven techniques to create optimized restart schedules have not yet been extensively studied.
Here, we present a simple scheduling approach that iteratively selects the algorithm performing best on the distribution of unsolved training problems at time of selection, resulting in a problem-independent solver schedule. We demonstrate our approach using well-known optimizers from numerical black-box optimization on the BBOB testbed, bridging much of the gap between single and virtual best solver from the original portfolio across various evaluation protocols. Our greedy restart schedule presents a powerful baseline for more complex dynamic algorithm selection models.
]]></description>
<dc:subject>numerical-methods optimization meta-optimization rather-interesting to-understand consider:stochastc-ones consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7d0cd9df7f70/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1810.02334">
    <title>[1810.02334] Unsupervised Learning via Meta-Learning</title>
    <dc:date>2020-05-21T11:52:09+00:00</dc:date>
    <link>https://arxiv.org/abs/1810.02334</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data. Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics. Instead, we develop an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data. To do so, we construct tasks from unlabeled data in an automatic way and run meta-learning over the constructed tasks. Surprisingly, we find that, when integrated with meta-learning, relatively simple task construction mechanisms, such as clustering embeddings, lead to good performance on a variety of downstream, human-specified tasks. Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the embedding learned by four prior unsupervised learning methods.
]]></description>
<dc:subject>meta-optimization unsupervised-learning rather-interesting novelty-search-kinda metaheuristics to-simulate to-write-about consider:generalizations consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:eb3a5c53990c/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1904.09813">
    <title>[1904.09813] Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms</title>
    <dc:date>2020-05-09T12:03:10+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.09813</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems.
]]></description>
<dc:subject>metaheuristics meta-optimization hedging rather-interesting algorithms to-write-about to-simulate consider:toy-examples consider:performance-measures</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:64336a2c070a/</dc:identifier>
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<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>
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<item rdf:about="https://arxiv.org/abs/1709.06129">
    <title>[1709.06129] When is a Convolutional Filter Easy To Learn?</title>
    <dc:date>2017-09-27T12:37:51+00:00</dc:date>
    <link>https://arxiv.org/abs/1709.06129</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input. We show that (stochastic) gradient descent with random initialization can learn the convolutional filter in polynomial time and the convergence rate depends on the smoothness of the input distribution and the closeness of patches. To the best of our knowledge, this is the first recovery guarantee of gradient-based algorithms for convolutional filter on non-Gaussian input distributions. Our theory also justifies the two-stage learning rate strategy in deep neural networks. While our focus is theoretical, we also present experiments that illustrate our theoretical findings.]]></description>
<dc:subject>machine-learning meta-optimization neural-networks convolutional-networks to-write-about consider:classification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:be168df0256c/</dc:identifier>
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<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>
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<item rdf:about="https://arxiv.org/abs/1708.05070">
    <title>[1708.05070] Data-driven Advice for Applying Machine Learning to Bioinformatics Problems</title>
    <dc:date>2017-08-27T12:27:48+00:00</dc:date>
    <link>https://arxiv.org/abs/1708.05070</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems.
]]></description>
<dc:subject>meta-optimization machine-learning benchmarking performance-measure feature-construction to-write-about hey-I-know-this-guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b10fc1086451/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1706.04119">
    <title>[1706.04119] From MEGATON to RASCAL: Surfing the Parameter Space of Evolutionary Algorithms</title>
    <dc:date>2017-06-20T17:14:50+00:00</dc:date>
    <link>https://arxiv.org/abs/1706.04119</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The practice of evolutionary algorithms involves a mundane yet inescapable phase, namely, finding parameters that work well. How big should the population be? How many generations should the algorithm run? What is the (tournament selection) tournament size? What probabilities should one assign to crossover and mutation? All these nagging questions need good answers if one is to embrace success. Through an extensive series of experiments over multiple evolutionary algorithm implementations and problems we show that parameter space tends to be rife with viable parameters. We aver that this renders the life of the practitioner that much easier, and cap off our study with an advisory digest for the weary.
]]></description>
<dc:subject>hey-I-know-this-guy machine-learning meta-optimization evolutionary-algorithms metaheuristics hyperheuristics to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fcd9c47b435e/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/1702.01780">
    <title>[1702.01780] Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming</title>
    <dc:date>2017-04-30T12:44:39+00:00</dc:date>
    <link>https://arxiv.org/abs/1702.01780</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Machine learning has been gaining traction in recent years to meet the demand for tools that can efficiently analyze and make sense of the ever-growing databases of biomedical data in health care systems around the world. However, effectively using machine learning methods requires considerable domain expertise, which can be a barrier of entry for bioinformaticians new to computational data science methods. Therefore, off-the-shelf tools that make machine learning more accessible can prove invaluable for bioinformaticians. To this end, we have developed an open source pipeline optimization tool (TPOT-MDR) that uses genetic programming to automatically design machine learning pipelines for bioinformatics studies. In TPOT-MDR, we implement Multifactor Dimensionality Reduction (MDR) as a feature construction method for modeling higher-order feature interactions, and combine it with a new expert knowledge-guided feature selector for large biomedical data sets. We demonstrate TPOT-MDR's capabilities using a combination of simulated and real world data sets from human genetics and find that TPOT-MDR significantly outperforms modern machine learning methods such as logistic regression and eXtreme Gradient Boosting (XGBoost). We further analyze the best pipeline discovered by TPOT-MDR for a real world problem and highlight TPOT-MDR's ability to produce a high-accuracy solution that is also easily interpretable.
]]></description>
<dc:subject>hey-I-know-this-guy bioinformatics machine-learning meta-optimization workflows framework</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f1ddc6939c05/</dc:identifier>
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<item rdf:about="http://biorxiv.org/content/early/2017/02/27/111989?rss=1">
    <title>STPGA: Selection of training populations with a genetic algorithm | bioRxiv</title>
    <dc:date>2017-03-06T12:04:02+00:00</dc:date>
    <link>http://biorxiv.org/content/early/2017/02/27/111989?rss=1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Optimal subset selection is an important task that has numerous algorithms designed for it and has many application areas. STPGA contains a special genetic algorithm supplemented with a tabu memory property (that keeps track of previously tried solutions and their fitness for a number of iterations), and with a regression of the fitness of the solutions on their coding that is used to form the ideal estimated solution (look ahead property) to search for solutions of generic optimal subset selection problems. I have initially developed the programs for the specific problem of selecting training populations for genomic prediction or association problems, therefore I give discussion of the theory behind optimal design of experiments to explain the default optimization criteria in STPGA, and illustrate the use of the programs in this endeavor. Nevertheless, I have picked a few other areas of application: supervised and unsupervised variable selection based on kernel alignment, supervised variable selection with design criteria, influential observation identification for regression, solving mixed integer quadratic optimization problems, balancing gains and inbreeding in a breeding population. Some of these illustrations pertain new statistical approaches.

]]></description>
<dc:subject>genetic-algorithm data-balancing coevolution statistics performance-measure meta-optimization rather-interesting to-write-about consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e43d35bad994/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1401.5861">
    <title>[1401.5861] Online Speedup Learning for Optimal Planning</title>
    <dc:date>2014-12-25T11:53:05+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.5861</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Domain-independent planning is one of the foundational areas in the field of Artificial Intelligence. A description of a planning task consists of an initial world state, a goal, and a set of actions for modifying the world state. The objective is to find a sequence of actions, that is, a plan, that transforms the initial world state into a goal state. In optimal planning, we are interested in finding not just a plan, but one of the cheapest plans. A prominent approach to optimal planning these days is heuristic state-space search, guided by admissible heuristic functions. Numerous admissible heuristics have been developed, each with its own strengths and weaknesses, and it is well known that there is no single "best heuristic for optimal planning in general. Thus, which heuristic to choose for a given planning task is a difficult question. This difficulty can be avoided by combining several heuristics, but that requires computing numerous heuristic estimates at each state, and the tradeoff between the time spent doing so and the time saved by the combined advantages of the different heuristics might be high. We present a novel method that reduces the cost of combining admissible heuristics for optimal planning, while maintaining its benefits. Using an idealized search space model, we formulate a decision rule for choosing the best heuristic to compute at each state. We then present an active online learning approach for learning a classifier with that decision rule as the target concept, and employ the learned classifier to decide which heuristic to compute at each state. We evaluate this technique empirically, and show that it substantially outperforms the standard method for combining several heuristics via their pointwise maximum.
]]></description>
<dc:subject>LOL NFL heuristics system-of-professions meta-optimization adaptive-control operations-research planning I-did-say-LOL-right? silo-based-life artificial-intelligence</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2fa74fa66ebd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:LOL"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:NFL"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-of-professions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:adaptive-control"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:operations-research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:I-did-say-LOL-right?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:silo-based-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-intelligence"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1411.0156">
    <title>[1411.0156] Surrogate Search As a Way to Combat Harmful Effects of Ill-behaved Evaluation Functions</title>
    <dc:date>2014-12-25T11:48:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1411.0156</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recently, several researchers have found that cost-based satisficing search with A* often runs into problems. Although some "work arounds" have been proposed to ameliorate the problem, there has been little concerted effort to pinpoint its origin. In this paper, we argue that the origins of this problem can be traced back to the fact that most planners that try to optimize cost also use cost-based evaluation functions (i.e., f(n) is a cost estimate). We show that cost-based evaluation functions become ill-behaved whenever there is a wide variance in action costs; something that is all too common in planning domains. The general solution to this malady is what we call a surrogatesearch, where a surrogate evaluation function that doesn't directly track the cost objective, and is resistant to cost-variance, is used. We will discuss some compelling choices for surrogate evaluation functions that are based on size rather that cost. Of particular practical interest is a cost-sensitive version of size-based evaluation function -- where the heuristic estimates the size of cheap paths, as it provides attractive quality vs. speed tradeoffs
]]></description>
<dc:subject>the-mangle-in-practice rather-interesting planning meta-optimization workarounds algorithms approximation nudge-targets heuristics stress-testing-in-the-wild consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:411f28adbb67/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:workarounds"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stress-testing-in-the-wild"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.7185">
    <title>[1412.7185] Parameter Selection In Particle Swarm Optimization For Transportation Network Design Problem</title>
    <dc:date>2014-12-24T13:33:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.7185</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In transportation planning and development, transport network design problem seeks to optimize specific objectives (e.g. total travel time) through choosing among a given set of projects while keeping consumption of resources (e.g. budget) within their limits. Due to the numerous cases of choosing projects, solving such a problem is very difficult and time-consuming. Based on particle swarm optimization (PSO) technique, a heuristic solution algorithm for the bi-level problem is designed. This paper evaluates the algorithm performance in the response of changing certain basic PSO parameters.
]]></description>
<dc:subject>metaheuristics meta-optimization PSO rediscovering-adaptive-algorithms parameter-tuning that-old-chestnut nudge-targets low-hanging-fruit consider:as-object-lesson-for-class</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0a154b59b8ed/</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:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:PSO"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rediscovering-adaptive-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parameter-tuning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:that-old-chestnut"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:low-hanging-fruit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:as-object-lesson-for-class"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.1913">
    <title>[1412.1913] A Portfolio Approach to Algorithm Selection for Discrete Time-Cost Trade-off Problem</title>
    <dc:date>2014-12-14T14:11:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.1913</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[It has been widely known that performance of algorithms for NP-Hard problems varies from instance to instance. This phenomenon has been observed, when we comprehensively studied multi-objective evolutionary algorithms (MOEAs) on a six benchmark instances of discrete time-cost trade-off problem (DTCTP). Instead of using single algorithm to solve DTCTP, we use a portfolio approach that takes multiple algorithms as its constituent. In this paper, we proposed portfolio comprising of four MOEAs, Non-dominated sorting genetic algorithm 2 (NSGA 2), the strength Pareto EA 2 (SPEA 2), Pareto archive evolutionary strategy (PAES) and Niched Pareto Genetic Algorithm 2 (NPGA 2) to solve DTCTP. The result shows that the portfolio approach is computationally fast and qualitatively superior than its constituent algorithms for all benchmark instances. Moreover, portfolio approach provides an insight in selecting the best algorithm for all instances of DTCTP.
]]></description>
<dc:subject>bandit-problems meta-optimization multiobjective-optimization performance-space-analysis the-mangle-in-practice nudge-targets algorithms rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f9af5717eed0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bandit-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-space-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1403.6863">
    <title>[1403.6863] Online Learning of k-CNF Boolean Functions</title>
    <dc:date>2014-08-01T11:40:52+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.6863</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper revisits the problem of learning a k-CNF Boolean function from examples in the context of online learning under the logarithmic loss. In doing so, we give a Bayesian interpretation to one of Valiant's celebrated PAC learning algorithms, which we then build upon to derive two efficient, online, probabilistic, supervised learning algorithms for predicting the output of an unknown k-CNF Boolean function. We analyze the loss of our methods, and show that the cumulative log-loss can be upper bounded, ignoring logarithmic factors, by a polynomial function of the size of each example.
]]></description>
<dc:subject>machine-learning online-learning nudge-targets algorithms meta-optimization switching-approaches consider:evolving-another-dozen</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:73e1d29d8a09/</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:online-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:switching-approaches"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:evolving-another-dozen"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.3016">
    <title>[1304.3016] Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach</title>
    <dc:date>2014-07-06T12:35:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.3016</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Interference mitigation has a great potential for improving the performance of interference limited wireless networks. In this paper we introduce novel schemes for interference management in wireless cellular networks using a virtual layer that captures and simplifies the complicated interference situation in the network. We show how a network utility maximization approach in conjunction with suitable short term scheduling and optimization in this virtual layer can be used for autonomous interference minimization by power control. We compare three distributed algorithms and evaluate their applicability for different user mobility assumptions. These algorithms gradually and autonomously steer the network towards a higher utility. Thereby the granularity of control ranges from controlling frequency subband power via controlling the power on a per-beam basis through to only enforcing average power constraints per beam. We use extensive system-level simulations which indicate high gains. In particular, it turns out that larger gains can be achieved by imposing average power constraints and allowing opportunistic scheduling instantaneously, rather than controlling the power in a strict way. Further we introduce a centralized version directly solving the underlying optimization and showing fast convergence, which serves as a performance benchmark for the distributed solutions. Finally, we investigate the deviation from global optimality by comparing to a Branch-And-Bound based solution.
]]></description>
<dc:subject>coordination collective-intelligence metaheuristics meta-optimization algorithms nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8ebb87e87c96/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coordination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:collective-intelligence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1401.7953">
    <title>[1401.7953] Training population selection for (breeding value) prediction</title>
    <dc:date>2014-02-26T11:25:52+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.7953</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Training population selection for genomic selection has captured a great deal of interest in animal and plant breeding. In this article we derive a computationally efficient statistic to evaulate the quality of a training set for a given test dataset. We adopt a genetic algorithm scheme to find a plausible training set for a given test dataset. Our statistic is related to the reliability measures from the the mixed model. Finally, we implement our algorithm on two datasets, namely, the FHB-Barley CAP dataset and Arabidopsis dataset.
]]></description>
<dc:subject>data-balancing machine-learning genetic-algorithm meta-optimization nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bc5e044ee60b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-balancing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-algorithm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.0903">
    <title>[1312.0903] Uniqueness in quadratic and hyperbolic 0-1 programming problems</title>
    <dc:date>2013-12-19T13:19:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.0903</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We analyze the question of deciding whether a quadratic or a hyperbolic 0-1 programming instance has a unique optimal solution. Both uniqueness questions are known to be NP-hard, but are unlikely to be contained in the class NP. We precisely pinpoint their computational complexity by showing that they both are complete for the complexity class...]]></description>
<dc:subject>computational-complexity feature-extraction classification-of-instances interesting nudge-targets consider:learning-by-watching meta-optimization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d2cd245e2aca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:computational-complexity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-extraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification-of-instances"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.3088">
    <title>[1311.3088] Endogenous games with goals: side-payments among goal-directed artificial agents</title>
    <dc:date>2013-12-16T23:07:29+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.3088</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Artificial agents are typically oriented to the realization of an externally assigned task and try to optimize over secondary aspects of plan execution such time lapse or power consumption, technically displaying a quasi-dichotomous preference relation. Boolean games have been developed as a paradigm for modelling societies of agents with this type of preference. In boolean games agents exercise control over propositional variables and strive to achieve a goal formula whose realization might require the opponents' cooperation. Recently, a theory of incentive engineering for such games has been devised, where an external authority steers the outcome of the game towards certain desirable properties consistent with players' goals, by imposing a taxation mechanism on the players that makes the outcomes that do not comply with those properties less appealing to them. The present contribution stems from a complementary perspective and studies, instead, how games with quasi-dichotomous preferences can be transformed from inside, rather than from outside, by endowing players with the possibility of sacrificing a part of their payoff received at a certain outcome in order to convince other players to play a certain strategy. Concretely we explore the properties of endogenous games with goals, obtained coupling strategic games with goals, a generalization of boolean games, with the machinery of endogenous games coming from game theory. We analyze equilibria in those structures, showing the preconditions needed for desirable outcomes to be achieved without external intervention. What our results show is that endogenous games with goals display specific irreducible features - with respect to what already known for endogenous games - which makes them worth studying in their own sake.
]]></description>
<dc:subject>emergent-design game-theory agent-based cat-herding nudge-targets meta-optimization interesting mechanism-design it-is-pretty-much-just-mechanism-design-isn't-it?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cc97ac4b740a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cat-herding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanism-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it-is-pretty-much-just-mechanism-design-isn't-it?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.3257">
    <title>[1303.3257] Ranking and combining multiple predictors without labeled data</title>
    <dc:date>2013-08-31T21:02:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.3257</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In a broad range of classification and decision making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier accuracy can be assessed using available labeled data, and raises two questions: given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to a) reliably rank them; and b) construct a meta-classifier more accurate than most classifiers in the ensemble? 
Here we present a novel spectral approach to address these questions. First, assuming conditional independence between classifiers, we show that the off-diagonal entries of their covariance matrix correspond to a rank-one matrix. Moreover, the classifiers can be ranked using the leading eigenvector of this covariance matrix, as its entries are proportional to their balanced accuracies. Second, via a linear approximation to the maximum likelihood estimator, we derive the Spectral Metal Learner (SML), a novel ensemble classifier whose weights are equal to this eigenvector entries. On both simulated and real data, SML typically achieves a higher accuracy than most classifiers in the ensemble and can provide a better starting point than majority voting, for estimating the maximum likelihood solution. Furthermore, SML is robust to the presence of small malicious groups of classifiers designed to veer the ensemble prediction away from the (unknown) ground truth.
]]></description>
<dc:subject>statistics learning-from-data meta-optimization machine-learning algorithms nudge-targets interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bea7799e3f05/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-from-data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<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:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://link.springer.com/article/10.1007/s00500-013-0995-9/fulltext.html">
    <title>Matching island topologies to problem structure in parallel evolutionary algorithms - Springer</title>
    <dc:date>2013-04-03T11:30:15+00:00</dc:date>
    <link>http://link.springer.com/article/10.1007/s00500-013-0995-9/fulltext.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In the context of Parallel Evolutionary Algorithms, it has been shown that different population structures induce different search performances. Nevertheless, no work has shown a clear cut evidence that there is a correlation between the solver’s population structure and the problem’s network structure. In this work, we verify this correlation performing a clear and systematic analysis of a large set of population structures (based on the well known β-graphs and NK-landscape problems. Furthermore, we go beyond our findings in these idealised experiments by analysing the performance of variable-topology EAs on a dynamic real-world problem, the Multi-Skills Call Centre.]]></description>
<dc:subject>evolutionary-algorithms meta-optimization heatmap performance-measure lunch-pricing nice</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e46fc37255ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evolutionary-algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heatmap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lunch-pricing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nice"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.2130">
    <title>[1303.2130] Convex Discriminative Multitask Clustering</title>
    <dc:date>2013-03-31T11:47:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.2130</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Multitask clustering tries to improve the clustering performance of multiple tasks simultaneously by taking their relationship into account. Most existing multitask clustering algorithms fall into the type of generative clustering, and none are formulated as convex optimization problems. In this paper, we propose two convex Discriminative Multitask Clustering (DMTC) algorithms to address the problems. Specifically, we first propose a Bayesian DMTC framework. Then, we propose two convex DMTC objectives within the framework. The first one, which can be seen as a technical combination of the convex multitask feature learning and the convex Multiclass Maximum Margin Clustering (M3C), aims to learn a shared feature representation. The second one, which can be seen as a combination of the convex multitask relationship learning and M3C, aims to learn the task relationship. The two objectives are solved in a uniform procedure by the efficient cutting-plane algorithm. Experimental results on a toy problem and two benchmark datasets demonstrate the effectiveness of the proposed algorithms.]]></description>
<dc:subject>reasonable-in-principle confused-in-practice meta-optimization machine-learning algorithms nudge-targets learning-by-watching learning-by-doing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0fe961d4f433/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reasonable-in-principle"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:confused-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<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:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-doing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.6055">
    <title>[1212.6055] On The Optimization of Dijkstras Algorithm</title>
    <dc:date>2013-03-03T21:20:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.6055</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper, we propose some amendment on Dijkstras algorithm in order to optimize it by reducing the number of iterations. The main idea is to solve the problem where more than one node satisfies the condition of the second step in the traditional Dijkstras algorithm. After application of the proposed modifications, the maximum number of iterations of Dijkstras algorithm is less than the number of the graphs nodes.]]></description>
<dc:subject>algorithms performance-measure meta-optimization amusing nudge-targets look-where-I'm-pointing-not-at-my-finger facepalm</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:537a0034342b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:amusing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:look-where-I'm-pointing-not-at-my-finger"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:facepalm"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.3494">
    <title>[1302.3494] On Polynomial Kernels for Sparse Integer Linear Programs</title>
    <dc:date>2013-03-03T14:08:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.3494</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Integer linear programs (ILPs) are a widely applied framework for dealing with combinatorial problems that arise in practice. It is known, e.g., by the success of CPLEX, that preprocessing and simplification can greatly speed up the process of optimizing an ILP. The present work seeks to further the theoretical understanding of preprocessing for ILPs by initiating a rigorous study within the framework of parameterized complexity and kernelization.]]></description>
<dc:subject>linear-programming algorithms the-mangle-in-practice representation classification nudge-targets meta-optimization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2a3f23975cb1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linear-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:the-mangle-in-practice"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.5406">
    <title>[1301.5406] Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species</title>
    <dc:date>2013-02-25T23:14:39+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.5406</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["...In Assemblathon 2, we provided a variety of sequence data to be assembled for three vertebrate species (a bird, a fish, and snake). This resulted in a total of 43 submitted assemblies from 21 participating teams. We evaluated these assemblies using a combination of optical map data, Fosmid sequences, and several statistical methods. From over 100 different metrics, we chose ten key measures by which to assess the overall quality of the assemblies. Conclusions - Many current genome assemblers produced useful assemblies, containing a significant representation of their genes, regulatory sequences, and overall genome structure. However, the high degree of variability between the entries suggests that there is still much room for improvement in the field of genome assembly and that approaches which work well in assembling the genome of one species may not necessarily work well for another."]]></description>
<dc:subject>algorithms horse-races meta-optimization multiobjective-optimization nudge-targets &quot;improvement&quot;-is-a-scary-word</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5a322a141528/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:horse-races"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:&quot;improvement&quot;-is-a-scary-word"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1112.4323">
    <title>[1112.4323] Between theory and practice: guidelines for an optimization scheme with genetic algorithms - Part I: single-objective continuous global optimization</title>
    <dc:date>2012-01-02T21:52:26+00:00</dc:date>
    <link>http://arxiv.org/abs/1112.4323</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The rapid advances in the field of optimization methods in many pure and applied science pose the difficulty of keeping track of the developments as well as selecting an appropriate technique that best suits the problem in-hand. From a practitioner point of view is rightful to wander "which optimization method is the best for my problem?". Looking at the optimization process as a "system" of intercon- nected parts, in this paper are collected some ideas about how to tackle an optimization problem using a class of tools from evolutionary computations called Genetic Algorithms. Despite the number of optimization techniques available nowadays the author of this paper thinks that Genetic Algorithms still play a central role for their versatility, robustness, theoretical framework and simplicity of use. The paper can be considered a "collection of tips" (from literature and personal experience) for the non-computer-scientist that has to deal with optimization problems both in the science and engineering practice. No original methods or algorithms are proposed.
]]></description>
<dc:subject>meta-optimization pragmatism-almost genetic-algorithm agile-almost project-management</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a0eb9f6c54ff/</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:pragmatism-almost"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-algorithm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agile-almost"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:project-management"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0812.3141">
    <title>[0812.3141] Choosing a penalty for model selection in heteroscedastic regression</title>
    <dc:date>2010-06-19T12:44:20+00:00</dc:date>
    <link>http://arxiv.org/abs/0812.3141</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We consider the problem of choosing between several models in least-squares regression with heteroscedastic data. We prove that any penalization procedure is suboptimal when the penalty is a function of the dimension of the model, at least for some typical heteroscedastic model selection problems. In particular, Mallows' Cp is suboptimal in this framework. On the contrary, optimal model selection is possible with data-driven penalties such as resampling or $V$-fold penalties. Therefore, it is worth estimating the shape of the penalty from data, even at the price of a higher computational cost. Simulation experiments illustrate the existence of a trade-off between statistical accuracy and computational complexity. As a conclusion, we sketch some rules for choosing a penalty in least-squares regression, depending on what is known about possible variations of the noise-level."
]]></description>
<dc:subject>statistics statistical-tests linear-regression meta-optimization nudge-targets multiobjective-optimization pragmatism-it-ain't</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a2085473faa6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistical-tests"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linear-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multiobjective-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pragmatism-it-ain't"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1006.1681">
    <title>[1006.1681] Towards the Design of Heuristics by Means of Self-Assembly</title>
    <dc:date>2010-06-19T12:42:01+00:00</dc:date>
    <link>http://arxiv.org/abs/1006.1681</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["…This idea arises from previous works in which computational models of self-assembly were subject to evolutionary design in order to perform the automatic construction of user-defined structures. Then, the aim of this paper is to present a novel methodology for the automated design of heuristics by means of self-assembly."
]]></description>
<dc:subject>hyperheuristics meta-optimization algorithms engineering-design design-automation nudge-targets nice</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:936794f1e260/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hyperheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:design-automation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nice"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>