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Noise pressure: systematic overestimation of population fitness in genetic algorithms with noisy fitness functions

Published: 07 July 2010 Publication History

Abstract

In applications of genetic algorithms (GA) to real world problems, we often encounter significant amounts of noise in our fitness functions. We show that the interaction of normally-distributed noise and selection pressure inherently cause overestimation of GA population fitness. We call this inherent fitness overestimation noise pressure. Furthermore, we show that oversampling is not a sufficient technique for eliminating noise pressure.

References

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Z. Davies, R. Gilbert, R. Merry, D. Kell, M. Theodorou, and G. Griffith. Efficient improvement of silage additives by using genetic algorithms. Applied and environmental microbiology, 66(4):1435, 2000.
[2]
Y. Jin and J. Branke. Evolutionary Optimization in Uncertain Environments-A Survey. IEEE Transactions on Evolutionary Computation, 9(3):303--317, 2005.
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F. Vandecasteele. Using genetic algorithms to optimize functions of microbial ecosystems. PhD thesis, University of Idaho, 2006.

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  1. Noise pressure: systematic overestimation of population fitness in genetic algorithms with noisy fitness functions

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    cover image ACM Conferences
    GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
    July 2010
    1520 pages
    ISBN:9781450300728
    DOI:10.1145/1830483

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 07 July 2010

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    1. genetic algorithm
    2. noise

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