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The micro-genetic operator in the search of global trends

Authors:

Flávio V.C. Martins,

Eduardo G. Carrano,

Elizabeth F. Wanner,

Ricardo H.C. TakahashiAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1125 - 1126

https://doi.org/10.1145/1389095.1389310

Published: 12 July 2008 Publication History

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Abstract

This work studies the mGA operator (Micro Genetic Algorithm), that has been proposed in literature as a "local search" operator for optimization with Genetic Algorithm. A new interpretation for this operator behavior is proposed, showing the role that this operator can have in a "global search". Such interpretation will possibly allow the definition of some directives for this operator parameter tuning, leading to more efficient GA that reach the optima with greater probability, spending less objective function evaluations. Some preliminary tests, conducted over problems of nonlinear functions with continuous variables, are presented, leading to some specific conjectures about what should be such directives.

References

[1]

S. A. Kazarlis, S. E. Papadakis, J. B. Theocharis, and V. Petridis. Microgenetic algorithms as generalized hill-climbing operators for GA optimization. IEEE Trans. Evol. Comput., 5(3):204--217, 2001.

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[2]

R. H. C. Takahashi, J. A. Vasconcelos, J. A. Ramirez, and L. Krahenbuhl. A multiobjective methodology for evaluating genetic operators. IEEE Trans. Magn., 37(5):3414--3417, 2003.

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Index Terms

The micro-genetic operator in the search of global trends
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 '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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

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Recommendations

A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems

Comparison of a crossover operator in binary-coded genetic algorithms

Hybridization of genetic algorithm and local search in multiobjective function optimization: recommendation of GA then LS

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