Genetic symbiosis algorithm for multiobjective optimization problem

Jiangming Mao, Kotaro Hirasawa, Takayuki Furuzuki, Junichi Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Evolutionary Algorithms are often well-suited for optimization problems. Since the mid-1980's, interest in multiobjective problems has been expanding rapidly. Various evolutionary algorithms have been developed which are capable of searching for multiple solutions concurrently in a single run. In this paper, we proposed a genetic symbiosis algorithm (GSA) for multi-object optimization problems (MOP) based on the symbiotic concept found widely in ecosystem. In the proposed GSA for MOP, a set of symbiotic parameters are introduced to modify the fitness of individuals used for reproduction so as to obtain a variety of Pareto solutions corresponding to user's demands. The symbiotic parameters are trained by minimizing a user defined criterion function. Several numerical simulations are carried out to demonstrate the effectiveness of proposed GSA.

Original languageEnglish
Title of host publicationRobot and Human Communication - Proceedings of the IEEE International Workshop
Pages137-142
Number of pages6
Publication statusPublished - 2000
Externally publishedYes
Event9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000 - Osaka
Duration: 2000 Sep 272000 Sep 29

Other

Other9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000
CityOsaka
Period00/9/2700/9/29

Fingerprint

Multiobjective optimization
Genetic algorithms
Evolutionary algorithms
Ecosystems
Computer simulation

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software

Cite this

Mao, J., Hirasawa, K., Furuzuki, T., & Murata, J. (2000). Genetic symbiosis algorithm for multiobjective optimization problem. In Robot and Human Communication - Proceedings of the IEEE International Workshop (pp. 137-142)

Genetic symbiosis algorithm for multiobjective optimization problem. / Mao, Jiangming; Hirasawa, Kotaro; Furuzuki, Takayuki; Murata, Junichi.

Robot and Human Communication - Proceedings of the IEEE International Workshop. 2000. p. 137-142.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mao, J, Hirasawa, K, Furuzuki, T & Murata, J 2000, Genetic symbiosis algorithm for multiobjective optimization problem. in Robot and Human Communication - Proceedings of the IEEE International Workshop. pp. 137-142, 9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000, Osaka, 00/9/27.
Mao J, Hirasawa K, Furuzuki T, Murata J. Genetic symbiosis algorithm for multiobjective optimization problem. In Robot and Human Communication - Proceedings of the IEEE International Workshop. 2000. p. 137-142
Mao, Jiangming ; Hirasawa, Kotaro ; Furuzuki, Takayuki ; Murata, Junichi. / Genetic symbiosis algorithm for multiobjective optimization problem. Robot and Human Communication - Proceedings of the IEEE International Workshop. 2000. pp. 137-142
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