Article

Directional self-learning of genetic algorithm

Authors:

Lin Cong,

Yuheng Sha,

Licheng Jiao,

Fang LiuAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 1569 - 1570

https://doi.org/10.1145/1068009.1068263

Published: 25 June 2005 Publication History

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Abstract

In order to overcome the low convergence speed and prematurity of classical genetic algorithm, an improved method named directional self-learning of genetic algorithm (DSLGA) is proposed in this paper. Through the self-learning operator directional information was introduced in local search process. The search direction was guided by the false derivative of the function fitness. Using the four operators among the individuals, the best solution was updated continuously. In experiments, DSLGA was tested on 4 unconstrained benchmark problems, and the results were compared with the algorithms presented recently. It showed that DSLGA performs much better than the other algorithms both in the quality of the solutions and in the computational complexity.

References

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Index Terms

Directional self-learning of genetic algorithm
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

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GECCO05

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GECCO05: Genetic and Evolutionary Computation Conference

June 25 - 29, 2005

Washington DC, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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