DocumentCode :
349641
Title :
A parallel genetic algorithm with distributed environment scheme
Author :
Miki, M. ; Hiroyasu, T. ; Kaneko, M. ; Hatanaka, K.
Author_Institution :
Dept. of Knowledge Eng., Doshisha Univ., Kyoto, Japan
Volume :
1
fYear :
1999
fDate :
1999
Firstpage :
695
Abstract :
Introduces an alternative approach to relieving the task of choosing optimal mutation and crossover rates by using a parallel and distributed GA with distributed environments. It is shown that the best mutation and crossover rates depend on the population sizes and the problems, and those are different between a single and multiple populations. The proposed distributed environment GA uses various combination of the parameters as the fixed values in the subpopulations. The excellent performance of the new scheme is experimentally recognized for a standard test function. It is concluded that the distributed environment GA is the fastest way to gain a good solution under the given population size and uncertainty of the appropriate crossover and mutation rates
Keywords :
genetic algorithms; parallel algorithms; crossover rates; distributed environment scheme; mutation rates; parallel genetic algorithm; population sizes; subpopulations; Adaptation model; Genetic algorithms; Genetic mutations; Knowledge engineering; Testing; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on
Conference_Location :
Tokyo
ISSN :
1062-922X
Print_ISBN :
0-7803-5731-0
Type :
conf
DOI :
10.1109/ICSMC.1999.814176
Filename :
814176
Link To Document :
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