A Self-adaptive Genetic Algorithm Based on Region Balance Variation

Author

Wang, Siyan ; Zhang, Guoli

Author_Institution

Coll. of Comput. Sci. & Technol., North China Electr. Power Univ., Baoding

fYear

2009

fDate

23-25 Jan. 2009

Firstpage

104

Lastpage

107

Abstract

Proposing a new algorithm which is simple but effective. Using characteristic of biological evolution and common sense to design the selection operator, improve the variation method of the crossover probability and the mutation probability. Numerical experiments show that the new algorithm is more effective than the comparative algorithm in realizing the high convergence speed, convergence precision, reducing the convergence generation and good at keeping the stability of the adaptive genetic algorithm.

Keywords

Gray codes; genetic algorithms; probability; biological evolution; common sense; crossover probability; gray code; mutation probability; region balance variation; selection operator; self-adaptive genetic algorithm; variation method; Binary codes; Convergence of numerical methods; Data mining; Educational institutions; Encoding; Genetic algorithms; Genetic mutations; Probability; Reflective binary codes; Stability; adaptive genetic algorithm; crossover; gray code; mutation; selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on

Conference_Location

Moscow

Print_ISBN

978-0-7695-3543-2

Type

conf

DOI

10.1109/WKDD.2009.167

Filename

4771889