DocumentCode :
1871887
Title :
Phenotypic genetic algorithm for partitioning problem
Author :
Tagawa, Kiyoharu ; Fukui, Tuyoshi ; Haneda, Hiromasa
Author_Institution :
Dept. of Electr. & Electron. Eng., Kobe Univ., Japan
fYear :
1997
fDate :
13-16 Apr 1997
Firstpage :
553
Lastpage :
556
Abstract :
The paper presents a phenotype based genetic algorithm for solving a partitioning problem, which is partitioning N objects into P groups to optimize an objective function. In the genetic algorithm, a phenotypic individual is represented by a way of division of a suffix set {1,…,N} into P subsets. In order to prevent premature convergence, the paper defines a distance between phenotypic individuals and uses it in the adaptive control of crossover rate. Furthermore, the paper proposes a new crossover operation named weighted edge crossover which preserves both the structure of phenotype and the desirable character of parents. These techniques perform well on a test problem: multiprocessor scheduling problem for robot control computation
Keywords :
adaptive control; combinatorial mathematics; genetic algorithms; multiprocessing systems; processor scheduling; robots; adaptive control; crossover rate; multiprocessor scheduling problem; objective function optimization; partitioning problem; phenotype based genetic algorithm; phenotypic genetic algorithm; phenotypic individuals; robot control computation; subsets; suffix set; weighted edge crossover; Adaptive control; Computational modeling; Convergence; Genetic algorithms; Optimization methods; Performance evaluation; Processor scheduling; Programmable control; Robot control; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 1997., IEEE International Conference on
Conference_Location :
Indianapolis, IN
Print_ISBN :
0-7803-3949-5
Type :
conf
DOI :
10.1109/ICEC.1997.592372
Filename :
592372
Link To Document :
بازگشت