Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
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
Guided rule discovery in XCS for high-dimensional classification problems
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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XCS is a learning classifier system that uses genetic algorithms to evolve a population of classifiers online. When applied to classification problems described by continuous attributes, XCS has demonstrated to be able to evolve classification models - represented as a set of independent interval-based rules - that are, at least, as accurate as those created by some of the most competitive machine learning techniques such as C4.5. Despite these successful results, analyses of how the different genetic operators affect the rule evolution for the interval-based rule representation are lacking. This paper focuses on this issue and conducts a systematic experimental analysis of the effect of the different genetic operators. The observations and conclusions drawn from the analysis are used as a tool for designing new operators that enable the system to extract models that are more accurate than those obtained by the original XCS scheme. More specifically, the system is provided with a new discovery component based on evolution strategies, and a new crossover operator is designed for both the original discovery component and the new one based on evolution strategies. In all these cases, the behavior of the new operators are carefully analyzed and compared with the ones provided by original XCS. The overall analysis enables us to supply important insights into the behavior of different operators and to improve the learning of interval-based rules in real-world domains on average.