Genetic symbiosis algorithm for multiobjective optimization problem

Jiangming Mao, Kotaro Hirasawa, Jinglu Hu, 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
Event9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000 - Osaka, Japan
Duration: Sep 27 2000Sep 29 2000

Other

Other9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000
CountryJapan
CityOsaka
Period9/27/009/29/00

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Software

Cite this

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