Genetic symbiosis algorithm for multiobjective optimization problem

Jiangming Mao, Kotaro Hirasawa, Jinglu Hu, Junichi Murata

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

7 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 publicationProceedings - 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000
Pages137-142
Number of pages6
DOIs
Publication statusPublished - Dec 1 2000
Event9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000 - Osaka, Japan
Duration: Sep 27 2000Sep 29 2000

Publication series

NameProceedings - IEEE International Workshop on Robot and Human Interactive Communication

Other

Other9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000
CountryJapan
CityOsaka
Period9/27/009/29/00

Fingerprint

Multiobjective optimization
Genetic algorithms
Evolutionary algorithms
Ecosystems
Computer simulation

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction

Cite this

Mao, J., Hirasawa, K., Hu, J., & Murata, J. (2000). Genetic symbiosis algorithm for multiobjective optimization problem. In Proceedings - 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000 (pp. 137-142). [892484] (Proceedings - IEEE International Workshop on Robot and Human Interactive Communication). https://doi.org/10.1109/ROMAN.2000.892484

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

Proceedings - 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000. 2000. p. 137-142 892484 (Proceedings - IEEE International Workshop on Robot and Human Interactive Communication).

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

Mao, J, Hirasawa, K, Hu, J & Murata, J 2000, Genetic symbiosis algorithm for multiobjective optimization problem. in Proceedings - 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000., 892484, Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, pp. 137-142, 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000, Osaka, Japan, 9/27/00. https://doi.org/10.1109/ROMAN.2000.892484
Mao J, Hirasawa K, Hu J, Murata J. Genetic symbiosis algorithm for multiobjective optimization problem. In Proceedings - 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000. 2000. p. 137-142. 892484. (Proceedings - IEEE International Workshop on Robot and Human Interactive Communication). https://doi.org/10.1109/ROMAN.2000.892484
Mao, Jiangming ; Hirasawa, Kotaro ; Hu, Jinglu ; Murata, Junichi. / Genetic symbiosis algorithm for multiobjective optimization problem. Proceedings - 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000. 2000. pp. 137-142 (Proceedings - IEEE International Workshop on Robot and Human Interactive Communication).
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