A parameter-less evolution strategy for global optimization

Authors:
Lino Costa
Affiliations:
Departamento de Produção e Sistemas, Escola de Engenharia, Universidade do Minho, Braga, Portugal
Venue:
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Year:
2006

Citing 3
Cited 1

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation

Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning

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Application domain study of evolutionary algorithms in optimization problems

Proceedings of the 10th annual conference on Genetic and evolutionary computation

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Abstract

Several evolutionary approaches have been applied to global optimization problems with significant success. Evolution strategies proved to be efficient global optimizers. However, these algorithms have several parameters which the setting is not simple. Thus, it is crucial to investigate how to set dynamically these parameters during the search. In this paper, a new parameter-less evolution strategy, which has only one single parameter to set, is proposed. This algorithm is compared with the traditional evolution strategies considering a set of difficult test problems. The results obtained indicate a promising performance of the new approach.