Title :
Genetic symbiosis algorithm
Author :
Hirasawa, K. ; Ishikawa, Y. ; Hu, J. ; Murata, J. ; Mao, J.
Author_Institution :
Dept. of Electr. & Electron. Syst. Eng., Kyushu Univ., Fukuoka, Japan
Abstract :
In this paper, a new genetic symbiosis algorithm (GSA) is proposed based on the symbiotic concept found widely in ecosystems. Since in the conventional genetic algorithms (GA) reproduction is done using only the fitness function of each individual, there are some problems such as premature convergence to an undesirable solution at a very early stage of generation. In addition in some GA applications, it is sometimes required to maintain diversified solutions and to find out many locally optimal solutions. GSA is proposed to solve these problems by considering mutual symbiotic relations between individuals. From simulations on optimizing a nonlinear function, it has been clarified that GSA can find more flexible solutions that can meet a variety of user´s requests than the conventional methods
Keywords :
convergence; genetic algorithms; search problems; ecosystems; fitness function; genetic symbiosis algorithm; locally optimal solutions; mutual symbiotic relations; nonlinear function; premature convergence; reproduction; search; Clustering methods; Ecosystems; Educational institutions; Genetic algorithms; Information science; Modeling; Optimization methods; Shape; Symbiosis; Systems engineering and theory;
Conference_Titel :
Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
Conference_Location :
La Jolla, CA
Print_ISBN :
0-7803-6375-2
DOI :
10.1109/CEC.2000.870813
