The Experts below are selected from a list of 13134 Experts worldwide ranked by ideXlab platform
Martin V. Butz - One of the best experts on this subject based on the ideXlab platform.
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JavaXCSF: the XCSF learning Classifier System in Java
ACM Sigevolution, 2010Co-Authors: Patrick O. Stalph, Martin V. ButzAbstract:Learning Classifier Systems were introduced by John H. Holland and constituted one of the first genetics-based machine learning techniques. The most prominent Learning Classifier System is XCS [3]. XCS can also be used for function approximation, then called XCSF [4]. JavaXCSF is an implementation of the XCSF Learning Classifier System. It is freely available from www.coboslab.psychologie.uni-wuerzburg.de/code/ Based on previous implementations, the code was extended and heavily restructured, resulting in a flexible framework that helps newcomers to understand the basic structure, but also allows developers to realize their own ideas in the XCSF System. For a detailed documentation, please refer to [2].
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improving the performance of a pittsburgh learning Classifier System using a default rule
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems, 2007Co-Authors: Jaume Bacardit, David E Goldberg, Martin V. ButzAbstract:An interesting feature of encoding the individuals of a Pittsburgh learning Classifier System as a decision list is the emergent generation of a default rule. However, performance of the System is strongly tied to the learning System choosing the correct class for this default rule. In this paper we experimentally study the use of an explicit (static) default rule. We first test simple policies for setting the class of the default rule, such as the majority/minority class of the problem. Next, we introduce some techniques to automatically determine the most suitable class.
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kernel based ellipsoidal conditions in the real valued xcs Classifier System
Genetic and Evolutionary Computation Conference, 2005Co-Authors: Martin V. ButzAbstract:Many learning Classifier System (LCS) implementations are restricted to the binary problem realm. Recently, the XCS Classifier System was enhanced to be able to handle real-valued inputs among others. In the real-valued enhancement, XCSF applies as a function approximation System that partitions the input space in hyperrectangular subspaces specified in the Classifiers. This paper changes the Classifier conditions to hyperspheres and hyperellipsoids and investigates the consequent performance impact. It is shown that the modifications yield improved performance in continuous functions. Even in discontinuous functions with parallel boundaries, XCS's performance does not degrade. Thus, for the real-valued problem domain, ellipsoidal condition structures can improve XCS's performance. From a more general perspective, this paper shows that XCS is readily applicable in diverse problem domains. To apply the System even more successfully, suitable kernel-based bases need to be found and used as Classifier conditions. XCS distributes the available structures over the problem space evolving more specialized structures in more complex problem subspaces.
David E Goldberg - One of the best experts on this subject based on the ideXlab platform.
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binary rule encoding schemes a study using the compact Classifier System
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems, 2007Co-Authors: Xavier Llorà, Kumara Sastry, David E GoldbergAbstract:Several binary rule encoding schemes have been proposed for Pittsburgh-style Classifier Systems. This paper focus on the analysis of how maximally general and accurate rules, regardless of the encoding, can be evolved in a such Classifier Systems. The theoretical analysis of maximally general and accurate rules using two different binary rule encoding schemes showed some theoretical results with clear implications to the scalability of any genetic-based machine learning System that uses the studied encoding schemes. Such results are clearly relevant since one of the binary representations studied is widely used on Pittsburgh-style Classifier Systems, and shows an exponential shrink of the useful rules available as the problem size increases . In order to be able to perform such analysis we use a simple barebones Pittsburgh Classifier System-- the compact Classifier System (CCS)--based on estimation of distribution algorithms.
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improving the performance of a pittsburgh learning Classifier System using a default rule
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems, 2007Co-Authors: Jaume Bacardit, David E Goldberg, Martin V. ButzAbstract:An interesting feature of encoding the individuals of a Pittsburgh learning Classifier System as a decision list is the emergent generation of a default rule. However, performance of the System is strongly tied to the learning System choosing the correct class for this default rule. In this paper we experimentally study the use of an explicit (static) default rule. We first test simple policies for setting the class of the default rule, such as the majority/minority class of the problem. Next, we introduce some techniques to automatically determine the most suitable class.
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GECCO - The compact Classifier System: motivation, analysis, and first results
Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05, 2005Co-Authors: Xavier Llorà, Kumara Sastry, David E GoldbergAbstract:This paper presents an initial analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style Classifier System. In order to be able to perform such analysis we introduce a simple bare-bones Pittsburgh Classifier Systems---the compact Classifier System (CCS)---based on estimation of distribution algorithms. Using a common rule encoding scheme of Pittsburgh Classifier Systems, CCS maintains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of the initially perturbed probability vectors which represents the rules. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally general and accurate rules.
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The compact Classifier System: scalability analysis and first results
2005 IEEE Congress on Evolutionary Computation, 2005Co-Authors: Xavier Llorà, Kumara Sastry, David E GoldbergAbstract:This paper presents an analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style Classifier System. In order to be able to perform such an analysis we introduce a simple bare-bones Pittsburgh-style Classifier Systems - the compact Classifier System (CCS) - based on estimation of distribution algorithms. Using a common rule encoding schemes of Pittsburgh-style Classifier Systems, CCS maintains a dynamic set of probability vectors that compactly describe a rule set. The compact genetic algorithm is used to evolve each of the initially perturbated probability vectors. Results show how CCS is able to evolve in a compact, simple, and elegant manner rule sets composed by maximally general and accurate rules. The initial theoretical analysis and results also show that traditional encoding schemes used by Pittsburgh-style Classifiers add an extra facet of difficulty. Such a bias plays a central role on the overall performance and scalability of CCS and other Pittsburgh-style Systems using such encoding schemes
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Learning Classifier Systems - What Is a Learning Classifier System
Lecture Notes in Computer Science, 2000Co-Authors: John H Holland, Lashon B Booker, Marco Colombetti, Marco Dorigo, David E Goldberg, Stephanie Forrest, Rick L Riolo, Robert E Smith, Pier Luca Lanzi, Wolfgang StolzmannAbstract:We asked "What is a Learning Classifier System" to some of the best-known researchers in the field. These are their answers.
Stewart W Wilson - One of the best experts on this subject based on the ideXlab platform.
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function approximation with a Classifier System
Genetic and Evolutionary Computation Conference, 2001Co-Authors: Stewart W WilsonAbstract:A Classifier System, XCSF, is introduced in which the prediction estimation mechanism is used to learn approximations to functions. The addition of weight vectors to the Classifiers allows piecewise-linear approximation. Results on functions of up to six dimensions show high accuracy. An interesting generalization of Classifier structure is suggested.
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Learning Classifier Systems - State of XCS Classifier System Research
Lecture Notes in Computer Science, 2000Co-Authors: Stewart W WilsonAbstract:XCS is a new kind of learning Classifier System that differs from the traditional kind primarily in its definition of Classifier fitness and its relation to contemporary reinforcement learning. Advantages of XCS include improved performance and an ability to form accurate maximal generalizations. This paper reviews recent research on XCS with respect to representation, internal state, predictive modeling, noise, and underlying theory and technique. A notation for environmental regularities is introduced.
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Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation, 2000Co-Authors: Pier Luca Lanzi, Stewart W WilsonAbstract:Wilson's (1994) bit-register memory scheme was incorporated into the XCS Classifier System and investigated in a series of non-Markov environments. Two extensions to the scheme were important in obtaining near-optimal performance in the harder environments. The first was an exploration strategy in which exploration of external actions was probabilistic as in Markov environments, but internal “actions” (register settings) were selected deterministically. The second was use of a register having more bit-positions than were strictly necessary to resolve environmental aliasing. The origins and effects of the two extensions are discussed.
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zcs a zeroth level Classifier System
Evolutionary Computation, 1994Co-Authors: Stewart W WilsonAbstract:A basic Classifier System, ZCS, is presented that keeps much of Holland's original framework but simplifies it to increase understandability and performance. ZCS's relation to Q-learning is brought out, and their performances compared in environments of two difficulty levels. Extensions to ZCS are proposed for temporary memory, better action selection, more efficient use of the genetic algorithm, and more general Classifier representation.
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Lookahead Planning and Latent Learning in a Classifier System
1991Co-Authors: Jean-arcady Meyer, Stewart W WilsonAbstract:Classifier Systems (CSs) have been used to simulate and describe the behavior of adaptive organisms, animats, and robots. However, Classifier System implementations to date have all been reactive Systems, which use simple S-R rules and which base their learning algorithms on trial-and-error reinforcement techniques similar to the Hullian Law of Effect. While these Systems have exhibited interesting behavior and good adaptive capacity, they cannot do other types of learning which require having explicit internal models of the external world, e.g., using complex plans as humans do, or doing “latent learning” of the type observed in rats. This paper describes a Classifier System that is able to learn and use internal models both to greatly decrease the time to learn general sequential decision tasks and to enable the System to exhibit latent learning.
Larry Bull - One of the best experts on this subject based on the ideXlab platform.
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An accuracy based corporate Classifier System
2020Co-Authors: Andy Tomlinson, Larry BullAbstract:Previously a corporate Classifier System has been implemented based on the ideas of Wilson and Goldberg which demonstrates that rule-linkage can, for a certain class of problems, offer benefits to a System based on the zeroth-level Classifier System (ZCS). In this work it is shown that similar benefits can be gained when similar rule-linkage mechanisms are applied to XCS. The intention is that the resultant System (CXCS) will exhibit XCS's generalization capabilities along with CCS's abilities to solve non-Markov tasks.
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Discrete and fuzzy dynamical genetic programming in the XCSF learning Classifier System
Soft Computing, 2014Co-Authors: Richard J. Preen, Larry BullAbstract: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.
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Learning Classifier Systems in Data Mining - Foreign Exchange Trading Using a Learning Classifier System
Studies in computational intelligence, 2008Co-Authors: Christopher Stone, Larry BullAbstract:We apply a simple Learning Classifier System to a foreign exchange trading problem. The performance of the Learning Classifier System is compared to that of a Genetic Programming approach from the literature.
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Towards Unconventional Computing through Simulated Evolution: Control of Nonlinear Media by a Learning Classifier System
Artificial Life, 2008Co-Authors: Larry Bull, Christopher Stone, Adam Budd, Ivan Uroukov, Ben De Lacy Costello, Andrew AdamatzkyAbstract:We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this initial study a light-sensitive subexcitable Belousov-Zhabotinsky reaction in which a checkerboard image, composed of cells of varying light intensity projected onto the surface of a thin silica gel impregnated with a catalyst and indicator, is controlled using a learning Classifier System. Pulses of wave fragments are injected into the checkerboard grid, resulting in rich spatiotemporal behavior, and a learning Classifier System is shown to be able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical Systems. Similarly, a learning Classifier System is shown to be able to control the electrical stimulation of cultured neuronal networks so that they display elementary learning. Results indicate that the learned stimulation protocols identify seemingly fundamental properties of in vitro neuronal networks. Use of another learning scheme presented in the literature confirms that such fundamental behavioral characteristics of a given network must be considered in training experiments.
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Using the XCS Classifier System for Multi-objective Reinforcement Learning Problems
Artificial Life, 2007Co-Authors: Matthew Studley, Larry BullAbstract:We investigate the performance of a learning Classifier System in some simple multi-objective, multi-step maze problems, using both random and biased action-selection policies for exploration. Results show that the choice of action-selection policy can significantly affect the performance of the System in such environments. Further, this effect is directly related to population size, and we relate this finding to recent theoretical studies of learning Classifier Systems in single-step problems.
Pier Luca Lanzi - One of the best experts on this subject based on the ideXlab platform.
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Learning Classifier Systems - A Roadmap to the Last Decade of Learning Classifier System Research
2020Co-Authors: Pier Luca Lanzi, Rick L RioloAbstract:In 1989 Wilson and Goldberg presented a critical review of the first ten years of learning Classifier System research. With this paper we review the subsequent ten years of learning Classifier Systems research, discussing the main achievements and the major research directions pursued in those years.
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Learning Classifier Systems - What Is a Learning Classifier System
Lecture Notes in Computer Science, 2000Co-Authors: John H Holland, Lashon B Booker, Marco Colombetti, Marco Dorigo, David E Goldberg, Stephanie Forrest, Rick L Riolo, Robert E Smith, Pier Luca Lanzi, Wolfgang StolzmannAbstract:We asked "What is a Learning Classifier System" to some of the best-known researchers in the field. These are their answers.
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what is a learning Classifier System
Lecture Notes in Computer Science, 2000Co-Authors: John H Holland, Lashon B Booker, Marco Colombetti, Marco Dorigo, David E Goldberg, Stephanie Forrest, Rick L Riolo, Robert E Smith, Pier Luca Lanzi, Wolfgang StolzmannAbstract:We asked "What is a Learning Classifier System" to some of the best-known researchers in the field. These are their answers.
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Toward Optimal Classifier System Performance in Non-Markov Environments
Evolutionary Computation, 2000Co-Authors: Pier Luca Lanzi, Stewart W WilsonAbstract:Wilson's (1994) bit-register memory scheme was incorporated into the XCS Classifier System and investigated in a series of non-Markov environments. Two extensions to the scheme were important in obtaining near-optimal performance in the harder environments. The first was an exploration strategy in which exploration of external actions was probabilistic as in Markov environments, but internal “actions” (register settings) were selected deterministically. The second was use of a register having more bit-positions than were strictly necessary to resolve environmental aliasing. The origins and effects of the two extensions are discussed.
