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An analysis of matching in learning classifier systems
2008
Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08
We investigate rule matching in learning classifier systems for problems involving binary and real inputs. We consider three rule encodings: the widely used character-based encoding, a specificity-based encoding, and a binary encoding used in Alecsys. We compare the performance of the three algorithms both on matching alone and on typical test problems. The results on matching alone show that the population generality influences the performance of the matching algorithms based on string
doi:10.1145/1389095.1389359
dblp:conf/gecco/ButzLLL08
fatcat:mhqe333fsrhkhlvbj3uhui2od4