Towards Canonical Learning Classifier Systems: Simple Accuracy, Payoff and Anticipatory Systems
نویسنده
چکیده
Learning Classifier Systems use evolutionary algorithms to facilitate rule-discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of an accuracy-based scheme where fitness is based on a rule's ability to predict the expected payoff from its use. Learning Classifier Systems which build anticipations of the expected states following their actions are also a focus of current research. This paper presents simple but effective learning classifier systems of each type with the aim of enabling the exploration of their basic principles, i.e., in isolation from the many other mechanisms they usually contain. The systems are described and modelled, before being implemented and tested.
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A Simple Accuracy-based Learning Classifier System
Learning Classifier Systems use evolutionary algorithms to facilitate rule-discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of accuracy-based fitness, after the introduction of XCS, where rule fitness is based on a rule's ability to predict the expected payoff from its use. Whilst XCS has been shown to ...
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