Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
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This paper presents a new algorithm for designing fuzzy systems. It automatically identifies the optimum number of rules in the fuzzy knowledge base and adjusts the parameters defining them.This algorithm hybridizes the robustness and capability of evolutive algorithms with multiobjective optimization techniques which are able to minimize both the prediction error of the fuzzy system and its complexity, i.e. the number of parameters. In order to guide the search and accelerate the algorithm's convergence, new specific genetic operators have been designed, which combine several heuristic and analytical methods. The results obtained show the validity of the proposed algorithm for the identification of fuzzy systems when applied to time-series prediction.