A New Self-adjusting Immune Genetic Algorithm

Authors:
Shaojie Qiao;Changjie Tang;Shucheng Dai;Mingfang Zhu;Binglun Zheng
Affiliations:
School of Computer Science, Sichuan University, Chengdu, China 610065 and School of Computing, National University of Singapore, Singapore 117590;School of Computer Science, Sichuan University, Chengdu, China 610065;School of Computer Science, Sichuan University, Chengdu, China 610065;School of Computer Science, Sichuan University, Chengdu, China 610065;School of Computer Science, Sichuan University, Chengdu, China 610065
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Abstract

The genetic algorithm based on immunity has recently been an appealing research methodology in evolutionary computation. Aiming to cope with the problems of genetic algorithms, i.e., the solution is apt to trap into a local optimum and the convergence speed is slow, this paper proposes a new self-adjusting immune genetic algorithm, called SaiGa (Self-adjusted immune Genetic algorithm), which seeks for an optimal solution with regard to complex problems such as the optimization of multidimensional functions by automatically tuning the crossover and the mutation probabilities, which can help avoid prematurity phenomena and maintain individual diversity. In particular, SaiGa introduces a variable optimization approach to improve the precision in terms of solving complex problems. The empirical results demonstrate that SaiGa can greatly accelerate convergence for finding an optimal solution compared with genetic algorithms and immune algorithms, achieve a better precision in function optimization, and avoid prematurity convergence.