Variable period adaptive genetic algorithm

Authors:
Ek Peng Chew;Chong Jin Ong;Kim Hee Lim
Affiliations:
Department of Industrial and Systems Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore;Department of Mechanical and Production Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore;Department of Industrial and Systems Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore
Venue:
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Year:
2002

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Abstract

Self-adapting genetic algorithm has two main factors contributing to its improved performance. The first is the effect of the progress of the evolution process where the fitness of the population improves as the number of generation increases. The second is the improvement due to the choice of the probabilities for the various genetic operators. In this paper, we propose a scheme that isolates the contributions of these two factors through the introduction of two competing populations. These two concurrent populations provide the necessary feedback to either prolong the duration of a good choice of the parameter setting or shorten that of a poor choice. Results from several numerical experiments have shown that the proposed scheme provides favorable performance over existing methods.