Convergence properties of non-crossover genetic algorithm

Author

Xiaoming, Dai ; Runmin, Zou ; Rong, Sun ; Rui, Feng ; Shao, Huihe

Author_Institution

Dept. of Autom., Shanghai Jiao Tong Univ., China

Volume

3

fYear

2002

fDate

2002

Firstpage

1822

Abstract

The canonical genetic algorithm (CGA) applies selection, crossover and mutation operators to solve difficult optimization problems. This paper introduces a new approach to CGA. It applies only mutation and selection operators. It is a non-crossover genetic algorithm (NCGA). The proof of global convergence of NCGA is presented in this paper. The simulation on the NP-complete traveling salesman problem (TSP) shows that NCGA is much faster than the CGA. In terms of computation efficiency, NCGA is a very promising approach. This paper casts doubt on the need of the crossover operator in GAs.

Keywords

Markov processes; convergence; genetic algorithms; travelling salesman problems; Markov chain; NP-complete; canonical genetic algorithm; crossover; global convergence; mutation; noncrossover genetic algorithm; optimization; selection; simulation; traveling salesman problem; Automation; Biological cells; Computational modeling; Convergence; Educational institutions; Genetic algorithms; Genetic mutations; Information science; Stochastic processes; Sun;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on

Print_ISBN

0-7803-7268-9

Type

conf

DOI

10.1109/WCICA.2002.1021397

Filename

1021397