Learning classifier systems

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This volume presents work from three consecutive editions of the International Workshop on Learning Classifier Systems held in Chicago (2003), Seattle (2004), and Washington (2005), hosted by the Genetic and Evolutionary Computation Conference. The topics covered reflect the diverse interests of the Learning Classifier Systems (LCS) community, ranging from theoretical analyses to practical applications in data mining. When organizing the contents, we opted for a thematic approach rather than a chronological one, allowing for a more coherent exploration of related areas. This decision was challenging, as the material could be categorized in various ways. Our taxonomy provides a structured grouping, though some works may fit into multiple categories. The first area focuses on knowledge representation, a crucial aspect of any learning system that influences both the learning capabilities and the mechanisms employed. Four chapters delve into different knowledge representations and the LCS methods used to manipulate them, highlighting their significance in the broader context of learning systems.