Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
A Micro-Genetic Algorithm for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Human evolutionary model: A new approach to optimization
Information Sciences: an International Journal
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
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Following earlier results, the purpose of this paper is to show a new evolutionary algorithm whose parameters are moving in ranges defined by experiments. That is to say, no parameters must be fixed at the beginning of the course of generations. Comparing the performance of two methods, we arrive to the conclusion that the random often is a better way.