A genetic algorithm with neutral mutations for solving nonstationary function optimization problems

Author

Ohkura, Kazuhiro ; Ueda, Kanji

Author_Institution

Dept. of Mech. Eng., Kobe Univ., Japan

fYear

1994

fDate

29 Nov-2 Dec 1994

Firstpage

248

Lastpage

252

Abstract

An extended genetic algorithm for solving nonstationary function optimization problems is presented. When using standard genetic algorithms, it is so difficult to deal with problems in which the population often fails to find or follow the changing optimum. This is due to the brittleness caused by the fact that the population tends to stay where it believes to be optimum. In order to overcome this unwanted phenomenon, a new string representation associated with inactive regions which enables one to adopt various types of neutral mutations is introduced. It is emphasized that this mechanism works effectively after obtaining directed evolution as an adaptive strategy. A 17-object knapsack problem is examined to discuss the dynamics of the extended genetic algorithm

Keywords

genetic algorithms; operations research; optimisation; problem solving; adaptive strategy; directed evolution; genetic algorithm; inactive regions; knapsack problem; neutral mutations; nonstationary function optimization problems; string representation; Adaptive systems; Degradation; Evolution (biology); Genetic algorithms; Genetic mutations; Mechanical engineering; Testing; Tree data structures;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Systems,1994. Proceedings of the 1994 Second Australian and New Zealand Conference on

Conference_Location

Brisbane, Qld.

Print_ISBN

0-7803-2404-8

Type

conf

DOI

10.1109/ANZIIS.1994.396967

Filename

396967