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Coordinate change operators for genetic algorithms

Authors:

Elizabeth F. Wanner,

Eduardo G. Carrano,

Ricardo H.C. TakahashiAuthors Info & Claims

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

Pages 1149 - 1150

https://doi.org/10.1145/1389095.1389322

Published: 12 July 2008 Publication History

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Abstract

This paper studies the issue of space coordinate change in genetic algorithms, based on two methods: convex quadratic approximations, and principal component analysis. In both methods, the procedure employs only the objective function samples that have already been obtained through the usual genetic algorithm operations, without the need of any additional function evaluation. The two procedures have been tested over a set of benchmark problems, and the data has been analyzed via a stochastic dominance analysis procedure. In both cases, the results suggest that in the transformed coordinates the genetic algorithm can able to deal with ill-conditioned problems in less iterations and with greater proportion of successful attempts, in comparison to the genetic algorithm without coordinate transformation.

References

[1]

E. Oja. Neural networks, principal component, and subspaces. International Journal of Neural Systems, 1:61--68, 1989.

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[2]

S. Pemmaraju and S. Skiena. Computational Discrete Mathematics: Combinatorics and Graph Theory with Mathematica . Cambridge, 2003.

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[3]

R. H. C. Takahashi, J. A. Vasconcellos, J. A. Ramirez, and L. Krahenbuhl. A multiobjective methodology for evaluation genetic operators. IEEE Transactions on Magnetics, 39:1321--1324, 2003.

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[4]

E. F. Wanner, F. G. Guimarães, R. H. C. Takahashi, and P. F. Fleming. Quadratic approximation-based coordinate change in genetic algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, CA, 2006. IEEE Press.

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(1997)Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)10.1109/CIFER.1997.618897(1)Online publication date: 1997
https://doi.org/10.1109/CIFER.1997.618897

Index Terms

Coordinate change operators for genetic algorithms
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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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

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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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(1997)Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr)10.1109/CIFER.1997.618897(1)Online publication date: 1997
https://doi.org/10.1109/CIFER.1997.618897

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Neural network crossover in genetic algorithms using genetic programming

Using messy genetic algorithms for solving the winner determination problem

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