An extensive PBIL algorithm with multiple traits and its application

Author

He, Zhenya ; Wei, Chengjian ; Zhang, Yieng ; Yang, Luxi

Author_Institution

Dept. of Radio Eng., Southeast Univ., Nanjing, China

Volume

1

fYear

2000

fDate

2000

Firstpage

777

Abstract

The population-based incremental learning (PBIL) algorithm is extended to a form where multiple traits for each gene reflect the pleiotropic and polygenic characteristics in natural evolved systems. This method is used to solve the traveling salesman problem. Some results are better than the best existing algorithms for evolutionary computation of the problem. The results show that the method proposed is comparable to the advanced level of solvers for the traveling salesman problem

Keywords

genetic algorithms; travelling salesman problems; unsupervised learning; competitive learning; evolutionary algorithms; evolutionary computation; extensive PBIL algorithm; gene; genetic algorithms; multiple traits; pleiotropic characteristics; polygenic characteristics; population-based incremental learning; probabilistic models; search space; traveling salesman problem; Bayesian methods; Buildings; Electronic design automation and methodology; Evolutionary computation; Genetic algorithms; Genetic mutations; Helium; Mutual information; Space exploration; Traveling salesman problems;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2000. Proceedings of the 2000 Congress on

Conference_Location

La Jolla, CA

Print_ISBN

0-7803-6375-2

Type

conf

DOI

10.1109/CEC.2000.870377

Filename

870377