Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
MILCS: a mutual information learning classifier system
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
New entropy model for extraction of structural information from XCS population
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Biasing evolving generations in learning classifier systems using information theoretic measures
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
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During the course of evolution in a genetic algorithm, and in particular a Learning Classifier System, we conjecture that there is benefit in using multiple fitness functions including information theoretic fitness measures. We discuss multiple ways to evaluate rules and groups of rules, some utilizing the data, and others without. For example, to evaluate the fitness of any rule against a set of data, we propose the use of information theoretic fitness measures Sufficiency and Quality, which are derived from the classical concepts of entropy and mutual information, in order to assign fitness values to classifiers. We present the experimental results for one of the methods, viz. evaluating individual rules against the data showing that their use reduces the number of generations needed to achieve peak performance.