Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection
Genetic Programming and Evolvable Machines
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Modeling selection pressure in XCS for proportionate and tournament selection
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Evolutionary rule-based systems for imbalanced data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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XCS is a complex machine learning technique that combines credit apportionment techniques for rule evaluation with genetic algorithms for rule discovery to evolve a distributed set of sub-solutions online. Recent research on XCS has mainly focused on achieving a better understanding of the reinforcement component, yielding several improvements to the architecture. Nonetheless, studies on the rule discovery component of the system are scarce. In this paper, we experimentally study the discovery component of XCS, which is guided by a steady-state genetic algorithm. We design a new procedure based on evolution strategies and adapt it to the system. Then, we compare in detail XCS with both genetic algorithms and evolution strategies on a large collection of real-life problems, analyzing in detail the interaction of the different genetic operators and their contribution in the search for better rules. The overall analysis shows the competitiveness of the new XCS based on evolution strategies and increases our understanding of the behavior of the different genetic operators in XCS.