Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Inductive learning of decision rules from attribute-based examples: a knowledge-intensive genetic algorithm approach
Recombination, selection, and the genetic construction of computer programs
Recombination, selection, and the genetic construction of computer programs
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
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
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Multiobjective genetic rule selection as a data mining postprocessing procedure
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multiobjective Optimization in Bioinformatics and Computational Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Observer-invariant histopathology using genetics-based machine learning
Natural Computing: an international journal
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Random artificial incorporation of noise in a learning classifier system environment
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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This paper analyzes the impact of using noisy data sets in Pittsburgh-style learning classifier systems. This study was done using a particular kind of learning classifier system based on multiobjective selection. Our goal was to characterize the behavior of this kind of algorithms when dealing with noisy domains. For this reason, we developed a theoretical model for predicting the minimal achievable error in noisy domains. Combining this theoretical model for crisp learners with graphical representations of the evolved hypotheses through multiobjective techniques, we are able to bound the behavior of a learning classifier system. This kind of modeling lets us identify relevant characteristics of the evolved hypotheses, such as overfitting conditions that lead to hypotheses that poorly generalize the concept to be learned.