Combined evolutionary algorithm for real parameters optimization

Jinn-Moon Yang*, C. Y. Kao

*Corresponding author for this work

Research output: Contribution to conferencePaper

20 Scopus citations

Abstract

The real coded genetic algorithms (RCGA) have proved to be more efficient than traditional bit-string genetic algorithm in parameter optimization, but the RCGA focuses on crossover operators and loss on the mutation operator for local search. Evolution strategies (ESs) and evolutionary programming (EP) only concern the Gaussian mutation operators. This paper proposes a technique, called combined evolutionary algorithm (CEA), by incorporating the ideas of EP and GAs into ES. Simultaneously, we add the local competition into the CEA in order to reduce the complexity and maintain the diversity. Over 20 benchmark function optimization problems are taken as benchmark problems. The results indicate that the CEA approach is a very powerful optimization technique.

Original languageEnglish
Pages732-737
Number of pages6
DOIs
StatePublished - 1 Jan 1996
EventProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96 - Nagoya, Jpn
Duration: 20 May 199622 May 1996

Conference

ConferenceProceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96
CityNagoya, Jpn
Period20/05/9622/05/96

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    Yang, J-M., & Kao, C. Y. (1996). Combined evolutionary algorithm for real parameters optimization. 732-737. Paper presented at Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, ICEC'96, Nagoya, Jpn, . https://doi.org/10.1109/ICEC.1996.542693