### 抄録

In this paper, a new Genetic Symbiosis Algorithm (GSA) is proposed based on the symbiotic concept found widely in ecosystems. Since in the conventional Genetic Algorithms (GA) reproduction is done using only the fitness function of each individual, there are some problems such as premature convergence to an undesirable solution at a very early stage of generation. In addition in some GA applications, it is sometimes required to maintain diversified solutions and to find out many locally optimal solutions. GSA is proposed to solve these problems by considering mutual symbiotic relations between Individuals. From simulations on optimizing a nonlinear function, it has been clarified that GSA can find more flexible solutions that can meet a variety of user's requests than the conventional methods.

元の言語 | English |
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ホスト出版物のタイトル | Proceedings of the IEEE Conference on Evolutionary Computation, ICEC |

ページ | 1377-1384 |

ページ数 | 8 |

巻 | 2 |

出版物ステータス | Published - 2000 |

外部発表 | Yes |

イベント | Proceedings of the 2000 Congress on Evolutionary Computation - California, CA, USA 継続期間: 2000 7 16 → 2000 7 19 |

### Other

Other | Proceedings of the 2000 Congress on Evolutionary Computation |
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市 | California, CA, USA |

期間 | 00/7/16 → 00/7/19 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)
- Computer Science(all)
- Computational Theory and Mathematics

### これを引用

*Proceedings of the IEEE Conference on Evolutionary Computation, ICEC*(巻 2, pp. 1377-1384)

**Genetic Symbiosis Algorithm.** / Hirasawa, K.; Ishikawa, Y.; Furuzuki, Takayuki; Murata, J.; Mao, J.

研究成果: Conference contribution

*Proceedings of the IEEE Conference on Evolutionary Computation, ICEC.*巻. 2, pp. 1377-1384, Proceedings of the 2000 Congress on Evolutionary Computation, California, CA, USA, 00/7/16.

}

TY - GEN

T1 - Genetic Symbiosis Algorithm

AU - Hirasawa, K.

AU - Ishikawa, Y.

AU - Furuzuki, Takayuki

AU - Murata, J.

AU - Mao, J.

PY - 2000

Y1 - 2000

N2 - In this paper, a new Genetic Symbiosis Algorithm (GSA) is proposed based on the symbiotic concept found widely in ecosystems. Since in the conventional Genetic Algorithms (GA) reproduction is done using only the fitness function of each individual, there are some problems such as premature convergence to an undesirable solution at a very early stage of generation. In addition in some GA applications, it is sometimes required to maintain diversified solutions and to find out many locally optimal solutions. GSA is proposed to solve these problems by considering mutual symbiotic relations between Individuals. From simulations on optimizing a nonlinear function, it has been clarified that GSA can find more flexible solutions that can meet a variety of user's requests than the conventional methods.

AB - In this paper, a new Genetic Symbiosis Algorithm (GSA) is proposed based on the symbiotic concept found widely in ecosystems. Since in the conventional Genetic Algorithms (GA) reproduction is done using only the fitness function of each individual, there are some problems such as premature convergence to an undesirable solution at a very early stage of generation. In addition in some GA applications, it is sometimes required to maintain diversified solutions and to find out many locally optimal solutions. GSA is proposed to solve these problems by considering mutual symbiotic relations between Individuals. From simulations on optimizing a nonlinear function, it has been clarified that GSA can find more flexible solutions that can meet a variety of user's requests than the conventional methods.

UR - http://www.scopus.com/inward/record.url?scp=0033666690&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033666690&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0033666690

VL - 2

SP - 1377

EP - 1384

BT - Proceedings of the IEEE Conference on Evolutionary Computation, ICEC

ER -