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Noise, fitness distribution, and selection intensity in genetic algorithms

Published: 12 July 2011 Publication History

Abstract

Many Genetic Algorithm (GA) problems have noisy fitness functions. In this paper, we describe a mathematical model of the noise distribution after selection and then show how this model of the noise distribution can be used to model the real, underlying selection intensity of the GA population, which promises to give us a better way to model GA convergence in the presence of noise.

References

[1]
H.-G. Beyer. Evolutionary algorithms in noisy environments: theoretical issues and guidelines for practice. Computer Methods in Applied Mechanics and Engineering, 239--267, 2000.
[2]
Y. Jin and J. Branke. Evolutionary Optimization in Uncertain Environments--A Survey. IEEE Transactions on Evolutionary Computation, 9(3):303--317, 2005.
[3]
J. B. S. Haldane. The Causes of Evolution. Longmans, New York, 1932.
[4]
H. Mühlenbein. The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3):303--346, 1997.

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    cover image ACM Conferences
    GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
    July 2011
    1548 pages
    ISBN:9781450306904
    DOI:10.1145/2001858

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 12 July 2011

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    Author Tags

    1. genetic algorithms
    2. noise
    3. selection intensity

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