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XCS's Strength-Based Twin. Part II

Tim Kovacs, XCS's Strength-Based Twin. Part II. Chapter in Learning Classifier Systems. ISBN 3-540-20544-6, pp. 81–98. January 2003. PDF, 374 Kbytes.


This sequel continues the comparison of the twins XCS and SB--XCS. We find they tend towards different representations of the solution and distinguish three types of representations which rule populations can form, namely complete maps, partial maps, and default hierarchies. Following this we evaluate the respective advantages and disadvantages of complete and partial maps at some length. We conclude that complete maps are likely to be superior for sequential tasks. For non-sequential tasks, partial maps have the advantage of parsimony whereas complete maps can take advantage of subsumption deletion. It is unclear which is more significant. We also conclude that partial maps are likely to be suitable for Pittsburgh classifier systems and supervised learning systems.

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