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Abstract: 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 and environments which will support strong overgeneral rules, demonstrate their dependence on the reward function, and give some... (Update)
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BibTeX entry: (Update)
@incollection{ kovacs00towards,
author = "Tim Kovacs",
title = "{Towards a theory of strong overgeneral classifiers}",
booktitle = "{Foundations of Genetic Algorithms Volume 6}",
publisher = "Morgan Kaufmann",
editor = "Worthy Martin and William M. Spears",
pages = "165--184",
year = "2000",
url = "citeseer.ist.psu.edu/437135.html" }
Citations (may not include all citations):2138 Genetic Algorithms in Search (context) - Goldberg - 1989
614 Reinforcement Learning: An Introduction - Sutton, Barto - 1998
85 Classifier Fitness Based on Accuracy - Wilson - 1995
45 ZCS: A Zeroth Level Classifier System - Wilson - 1994
10 Adding Temporary Memory to ZCS (context) - Cliff, Ross - 1995
10 Empirical Studies of Default Hierarchies and Sequences of Ru.. (context) - Riolo - 1988
9 Default Hierarchy Formation and Memory Exploitation in Learn.. (context) - Smith - 1991
2 Strength or Accuracy (context) - Kovacs - 2000
1 Forthcoming PhD Thesis (context) - Kovacs - 2001
Documents on the same site (ftp://ftp.cs.bham.ac.uk/pub/authors/T.Kovacs/publications.list.html): More
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