A New Genetic Algorithm Based on Negative Selection

Author

Li, Na-Na ; Gu, Jun-hua ; Liu, Bo-ying

Author_Institution

Sch. of Electron. & Inf. Eng., Tianjin Univ.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

4297

Lastpage

4299

Abstract

Genetic algorithm offers the common frame of resolving optimization problem by imitating biological evolution based on natural selection. However it has some drawbacks such as slow convergence and being premature. In genetic algorithm, individual generated by genetic operation is a bit random and even sometimes more inferior than its parents. So a new operator - negative selection that can filtrate bad-quality individual is introduced to genetic algorithm to speed up its speed of convergence and improve its global searching ability. With this new operator, a new optimization algorithm based genetic algorithm and negative selection is proposed. Furthermore this paper shows its ability to solve the function optimization problem

Keywords

genetic algorithms; search problems; biological evolution; function optimization problem; genetic algorithm; global searching ability; natural selection; negative selection; Biological system modeling; Bones; Computational biology; Convergence; Cybernetics; Evolution (biology); Genetic algorithms; Genetic engineering; Genetic mutations; Immune system; Libraries; Machine learning; Machine learning algorithms; Genetic algorithm; function optimization; immune system; negative selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.259016

Filename

4028828