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    <title>[1506.04002] Knowledge Representation in Learning Classifier Systems: A Review</title>
    <dc:date>2015-09-12T12:41:31+00:00</dc:date>
    <link>http://arxiv.org/abs/1506.04002</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Knowledge representation is a key component to the success of all rule based systems including learning classifier systems (LCSs). This component brings insight into how to partition the problem space what in turn seeks prominent role in generalization capacity of the system as a whole. Recently, knowledge representation component has received great deal of attention within data mining communities due to its impacts on rule based systems in terms of efficiency and efficacy. The current work is an attempt to find a comprehensive and yet elaborate view into the existing knowledge representation techniques in LCS domain in general and XCS in specific. To achieve the objectives, knowledge representation techniques are grouped into different categories based on the classification approach in which they are incorporated. In each category, the underlying rule representation schema and the format of classifier condition to support the corresponding representation are presented. Furthermore, a precise explanation on the way that each technique partitions the problem space along with the extensive experimental results is provided. To have an elaborated view on the functionality of each technique, a comparative analysis of existing techniques on some conventional problems is provided. We expect this survey to be of interest to the LCS researchers and practitioners since it provides a guideline for choosing a proper knowledge representation technique for a given problem and also opens up new streams of research on this topic.
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<dc:subject>learning-classifier-systems representation rather-interesting feature-discovery nudge-targets</dc:subject>
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    <title>[1204.4200] Discrete Dynamical Genetic Programming in XCS</title>
    <dc:date>2012-04-21T13:41:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.4200</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using a discrete dynamical system representation within the XCS Learning Classifier System. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such discrete dynamical systems within XCS to solve a number of well-known test problems."]]></description>
<dc:subject>genetic-programming learning-classifier-systems representation-theory design-patterns boolean-networks nudge-targets nice</dc:subject>
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    <title>[1204.4202] Fuzzy Dynamical Genetic Programming in XCSF</title>
    <dc:date>2012-04-20T18:23:25+00:00</dc:date>
    <link>http://arxiv.org/abs/1204.4202</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigation into using a fuzzy DGP representation within the XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic Networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such fuzzy dynamical systems within XCSF to solve several well-known continuous-valued test problems."]]></description>
<dc:subject>learning-classifier-systems genetic-programming fuzzy-math dynamical-control rules-learning nudge-targets</dc:subject>
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    <title>[1201.5604] Discrete and Fuzzy Dynamical Genetic Programming in the XCSF Learning Classifier System</title>
    <dc:date>2012-01-30T21:40:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1201.5604</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF Learning Classifier System. In particular, asynchronous Random Boolean Networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous Fuzzy Logic Networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems."]]></description>
<dc:subject>Kauffman-networks learning-classifier-systems genetic-programming nudge-targets interesting</dc:subject>
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