Two Views of Classifier Systems

Tim Kovacs, Lanzi P. L., Stolzmann W., Wilson S. W.

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

6 Citations (Scopus)


This work suggests two ways of looking at Michigan classifier systems; as Genetic Algorithm-based systems, and as Reinforcement Learning-based systems, and argues that the former is more suitable for traditional strength-based systems while the latter is more suitable for accuracy-based XCS. The dissociation of the Genetic Algorithm from policy determination in XCS is noted, and the two types of Michigan classifier system are contrasted with Pittsburgh systems.
Translated title of the contributionTwo Views of Classifier Systems
Original languageEnglish
Title of host publicationAdvances in Learning Classifier Systems
ISBN (Print)3540437932
Publication statusPublished - 2002

Bibliographical note

Other page information: 74-87
Other identifier: 1000647


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