A Simple Accuracy-based Learning Classifier System
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 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 be extremely effective in a number of domains, its complexity can make it difficult to establish clear reasons for its behaviour. This paper presents a simple accuracy-based learning classifier system with which to explore aspects of accuracy-based fitness in general. The system is described and modelled, before being implemented and tested on the multiplexer task.
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