Genetic symbiosis algorithm for multiobjective optimization problem

Jiangming Mao, Kotaro Hirasawa, Takayuki Furuzuki, 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 - IEEE International Workshop on Robot and Human Interactive Communication
Pages137-142
Number of pages6
DOIs
Publication statusPublished - 2000
Externally publishedYes
Event9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000 - Osaka
Duration: 2000 Sep 272000 Sep 29

Other

Other9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000
CityOsaka
Period00/9/2700/9/29

Fingerprint

Multiobjective optimization
Genetic algorithms
Evolutionary algorithms
Ecosystems
Computer simulation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction

Cite this

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

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

Proceedings - IEEE International Workshop on Robot and Human Interactive Communication. 2000. p. 137-142 892484.

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 Proceedings - IEEE International Workshop on Robot and Human Interactive Communication., 892484, pp. 137-142, 9th IEEE International Workshop on Robot and Human Interactive Communication, IEEE RO-MAN 2000, Osaka, 00/9/27. https://doi.org/10.1109/ROMAN.2000.892484
Mao J, Hirasawa K, Furuzuki T, Murata J. Genetic symbiosis algorithm for multiobjective optimization problem. In Proceedings - IEEE International Workshop on Robot and Human Interactive Communication. 2000. p. 137-142. 892484 https://doi.org/10.1109/ROMAN.2000.892484
Mao, Jiangming ; Hirasawa, Kotaro ; Furuzuki, Takayuki ; Murata, Junichi. / Genetic symbiosis algorithm for multiobjective optimization problem. Proceedings - IEEE International Workshop on Robot and Human Interactive Communication. 2000. pp. 137-142
@inproceedings{a50f4955fd9240efa25168f57adb8903,
title = "Genetic symbiosis algorithm for multiobjective optimization problem",
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.",
author = "Jiangming Mao and Kotaro Hirasawa and Takayuki Furuzuki and Junichi Murata",
year = "2000",
doi = "10.1109/ROMAN.2000.892484",
language = "English",
isbn = "078036273X",
pages = "137--142",
booktitle = "Proceedings - IEEE International Workshop on Robot and Human Interactive Communication",

}

TY - GEN

T1 - Genetic symbiosis algorithm for multiobjective optimization problem

AU - Mao, Jiangming

AU - Hirasawa, Kotaro

AU - Furuzuki, Takayuki

AU - Murata, Junichi

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84876956154&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876956154&partnerID=8YFLogxK

U2 - 10.1109/ROMAN.2000.892484

DO - 10.1109/ROMAN.2000.892484

M3 - Conference contribution

AN - SCOPUS:84876956154

SN - 078036273X

SN - 9780780362734

SP - 137

EP - 142

BT - Proceedings - IEEE International Workshop on Robot and Human Interactive Communication

ER -