Triggered Rule Discovery in Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
Bayesian estimation of rule accuracy in UCS
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
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A number of heuristics have been used in Learning Classifier Systems to initialise parameters of new rules, to adjust fitness of parent rules when they generate offspring, and to select rules for deletion. Some have not been studied in the literature before. We study the interaction of these heuristics in an attempt to improve performance and detect any unnecessary methods. We evaluate the two published methods for initialisation of new rules in XCS and find the one based on parental values results in better evolutionary search but larger population sizes than the one based on population means. In preliminary work we demonstrate that when the difficulty of the 6 multiplexer is increased by reducing the population size limit and turning off subsumption we can improve performance by discounting the fitness of both parents and children.