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The effects of selection on noisy fitness optimization

Published: 12 July 2011 Publication History

Abstract

This paper examines how the choice of the selection mechanism in an evolutionary algorithm impacts the objective function it optimizes, specifically when the fitness function is noisy. We provide formal results showing that, in an abstract infinite-population model, proportional selection optimizes expected fitness, truncation selection optimizes order statistics, and tournament selection can oscillate. The "winner" in a population depends on the choice of selection rule, especially when fitness distributions differ between individuals resulting in variable risk. These findings are further developed through empirical results on a novel stochastic optimization problem called "Die4", which, while simple, extends existing benchmark problems by admitting a variety of interpretations of optimality.

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J. Branke and C. Schmidt. Selection in the presence of noise. In Genetic and Evolutionary Computation: GECCO 2003, Lecture Notes in Computer Science, pages 766--777. Springer Berlin / Heidelberg, 2003.
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  • (2016)CEMAB: A Cross-Entropy-based Method for Large-Scale Multi-Armed BanditsArtificial Life and Computational Intelligence10.1007/978-3-319-51691-2_30(353-365)Online publication date: 27-Dec-2016

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cover image ACM Conferences
GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
July 2011
2140 pages
ISBN:9781450305570
DOI:10.1145/2001576
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Publication History

Published: 12 July 2011

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Author Tags

  1. convergence analysis
  2. evolutionary noisy optimization
  3. selection algorithms

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View all
  • (2016)CEMAB: A Cross-Entropy-based Method for Large-Scale Multi-Armed BanditsArtificial Life and Computational Intelligence10.1007/978-3-319-51691-2_30(353-365)Online publication date: 27-Dec-2016

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