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—In this paper, we take initial steps toward a theory of generalization and learning in the learning classifier system XCS. We start from Wilson's generalization hypothesis, which states that XCS has an intrinsic tendency to evolve accurate, maximally general classifiers. We analyze the different evolutionary pressures in XCS and derive a simple equation(More)
We analyse the concept of strong overgeneral rules, the Achilles' heel of traditional Michigan-style learning classifier systems, using both the traditional strength-based and newer accuracy-based approaches to rule fitness. We argue that different definitions of overgenerality are needed to match the goals of the two approaches, present minimal conditions(More)
Evolutionary Learning Classifier Systems (LCSs) combine reinforcement learning or supervised learning with effective genetics-based search techniques. Together these two mechanisms enable LCSs to evolve solutions to decision problems in the form of easy to interpret rules called classifiers. Although LCSs have shown excellent performance on some data mining(More)
Artificial Immune Systems (AIS) have been shown to be useful, practical and realisable approaches to real-world problems [5]. Most AIS implementations are based around a canonical algorithm such as clonotypic learning [4], which we may think of as individual, lifetime learning. Yet a species also learns. Gene libraries are often thought of as a biological(More)