Adjusting population distance for the dual-population genetic algorithm

Authors:
Taejin Park;Ri Choe;Kwang Ryel Ryu
Affiliations:
Department of Computer Engineering, Pusan National University, Busan, Korea;Department of Computer Engineering, Pusan National University, Busan, Korea;Department of Computer Engineering, Pusan National University, Busan, Korea
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

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Abstract

A dual-population genetic algorithm (DPGA) is a new multipopulation genetic algorithm that solves problems using two populations with different evolutionary objectives. The main population is similar to that of an ordinary genetic algorithm, and it evolves in order to obtain suitable solutions. The reserve population evolves to maintain and offer diversity to the main population. The two populations exchange genetic materials using interpopulation crossbreeding. This paper proposes a new fitness function of the reserve population based on the distance to the main populations. The experimental results have shown that the performance of DPGA is highly related to the distance between the populations and that the best distance differs for each problem. Generally, it is difficult to decide the best distance between the populations without prior knowledge about the problem. Therefore, this paper also proposes a method to dynamically adjust the distance between the populations using the distance between good parents, i.e., the parents that generated good offspring.