MILCS: a mutual information learning classifier system
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Information theoretic fitness measures for learning classifier systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
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This paper presents information-theoretic ideas to modify the course of evolution in Learning Classifier Systems. This approach exploits the possibilities that individuals in each generation contain potentially useful information that is not currently utilized. In particular, we look at the Sufficiency measure of a rule as an information theoretic indicator. We propose the modification of the XCS algorithm using this in the early formative stages of each run in view of these additional indicators of usefulness. The probability of selection during that period would be based on sufficiency. Preliminary simulation results show that the new approach reduces the effort needed to solve the 20-input multiplexer problem.