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Rule Fitness and Pathology in Learning Classifier Systems
2004
Evolutionary Computation
It has long been known that in some relatively simple reinforcement learning tasks traditional strength-based classifier systems will adapt poorly and show poor generalisation. In contrast, the more recent accuracy-based XCS, appears both to adapt and generalise well. In this work, we attribute the difference to what we call strong over general and fit over general rules. We begin by developing a taxonomy of rule types and considering the conditions under which they may occur. In order to do so
doi:10.1162/106365604773644341
pmid:15096307
fatcat:amt7ba5io5dp3nx6psfiokf24y