Neurocomputing: foundations of research
Evolving artificial intelligence
Evolving artificial intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Fast rule matching for learning classifier systems via vector instructions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Mining breast cancer data with XCS
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Tournament selection: stable fitness pressure in XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Data mining in learning classifier systems: comparing XCS with GAssist
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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An extended classifier system (XCS) is an adaptive rule-based technique that uses evolutionary search and reinforcement learning to evolve complete, accurate, and maximally general payoff map of an environment. The payoff map is represented by a set of condition-action rules called classifiers. Despite this insight, till now parameter-setting problem associated with LCS/XCS has important drawbacks. Moreover, the optimal values of some parameters are strongly influenced by properties of the environment like its complexity, changeability, and the level of noise. The aim of this paper is to overcome some of these difficulties by a self-adaptation of a learning rate parameter, which plays a key role in reinforcement learning, since it is used for updates of classifier parameters: prediction, prediction error, fitness, and action set estimation. Self-adaptive control of prediction learning rate is investigated in the XCS, whereas the fitness and error learning rates remain fixed. Simultaneous self-adaptation of prediction learning rate and mutation rate also undergo experiments. Self-adaptive XCS solves one-step problems in noisy and dynamic environments.