Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Analysis of the initialization stage of a Pittsburgh approach learning classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The compact classifier system: motivation, analysis, and first results
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Classifier fitness based on accuracy
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Guided rule discovery in XCS for high-dimensional classification problems
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Information Sciences: an International Journal
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Improving the performance of the BioHEL learning classifier system
Expert Systems with Applications: An International Journal
A new hybrid metaheuristic for medical data classification
International Journal of Metaheuristics
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This paper proposes a new smart crossover operator for a Pittsburgh Learning Classifier System. This operator, unlike other recent LCS approaches of smart recombination, does not learn the structure of the domain, but it merges the rules of N parents (N ≥ 2) to generate a new offspring. This merge process uses an heuristic that selects the minimum subset of candidate rules that obtains maximum training accuracy. Moreover the operator also includes a rule pruning scheme to avoid the inclusion of over-specific rules, and to guarantee as much as possible the robust behaviour of the LCS. This operator takes advantage from the fact that each individual in a Pittsburgh LCS is a complete solution, and the system has a global view of the solution space that the proposed rule selection algorithm exploits. We have empirically evaluated this operator using a recent LCS called GAssist. First with the standard LCS benchmark, the 11 bits multiplexer, and later using 25 standard real datasets. The results of the experiments over these datasets indicate that the new operator manages to increase the accuracy of the system over the classical crossover in 16 of the 25 datasets, and never having a significantly worse performance than the classic operator.