Atavistic strategy for genetic algorithm

Authors:
Dongmei Lin;Xiaodong Li;Dong Wang
Affiliations:
Center of Information and Education Technology, Foshan University, Foshan, China;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia;Department of Computer Science and Technology, Foshan University, Foshan, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Year:
2011

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Abstract

Atavistic evolutionary strategy for genetic algorithm is put forward according to the atavistic phenomena existing in the process of biological evolution, and the framework of the new strategy is given also. The effectiveness analysis of the new strategy is discussed by three characteristics of the reproduction operators. The introduction of atavistic evolutionary strategy is highly compatible with the minimum induction pattern, and increases the population diversity to a certain extent. The experimental results show that the new strategy improves the performance of genetic algorithm on convergence time and solution quality.