A New Genetic Algorithm Using Large Mutation Rates and Population-Elitist Selection (GALME)

Authors:
Hisashi Shimodaira
Affiliations:
-
Venue:
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Year:
1996

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Abstract

Genetic algorithms (GA' S) are one of promising means for function optimization. Methods for function optimization are required to perform local search as well as global search in a balanced way It is, however recognized that the traditional GA is not well suited to local search. I have tested algorithms combining various ideas to develop a new genetic algorithm to obtain the global optimum effectively As a result, it is turned out that the performance of a genetic algorithm using large mutation rates and population-enlist selection (GALME) is superior. This paper describes the GALME and its theoretical justification, and presents the results of experiments, compared to the traditional GA. With the range of the experiments, it is turned out that the performance of GALKE is remarkably superior to that of the traditional GA.