Multi-modal function optimization problem for evolutionary algorithm

Author

Hao, Pan ; Jingling, Yuan ; Luo, Zhong

Author_Institution

Wuhan Univ. of Technol., China

fYear

2003

fDate

3-5 Nov. 2003

Firstpage

157

Lastpage

160

Abstract

In this paper, a new algorithm for solving multimodal function optimization problems - two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained.

Keywords

evolutionary computation; parallel algorithms; search problems; GT algorithm; global recombination searching; intrinsic parallelism; multimodal function optimization problems; niche evolutionary strategy; population strategy; subspace local searching; subspace searching; two-level subspace evolutionary algorithm; Artificial immune systems; Artificial intelligence; Boolean functions; Evolutionary computation; Functional programming; Genetic programming; Roentgenium; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on

ISSN

1082-3409

Print_ISBN

0-7695-2038-3

Type

conf

DOI

10.1109/TAI.2003.1250184

Filename

1250184