Genetic symbiosis algorithm for multiobjective optimization problem

Jiangming Mao, Kotaro Hirasawa, Jinglu Hu, Junichi Murata

研究成果: Paper査読

6 被引用数 (Scopus)

抄録

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.

本文言語English
ページ137-142
ページ数6
出版ステータスPublished - 2000 12 1
外部発表はい
イベント9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000 - Osaka, Japan
継続期間: 2000 9 272000 9 29

Conference

Conference9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000
CountryJapan
CityOsaka
Period00/9/2700/9/29

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software

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