Graceful scaling on uniform versus steep-tailed noise

Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Andrew M. Sutton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Recently, different evolutionary algorithms (EAs) have been analyzed in noisy environments. The most frequently used noise model for this was additive posterior noise (noise added after the fitness evaluation) taken from a Gaussian distribution. In particular, for this setting it was shown that the (μ + 1)-EA on OneMax does not scale gracefully (higher noise cannot efficiently be compensated by higher μ). In this paper we want to understand whether there is anything special about the Gaussian distribution which makes the (μ + 1)-EA not scale gracefully. We keep the setting of posterior noise, but we look at other distributions. We see that for exponential tails the (μ + 1)-EA on OneMax does also not scale gracefully, for similar reasons as in the case of Gaussian noise. On the other hand, for uniform distributions (as well as other, similar distributions) we see that the (μ + 1)-EA on OneMax does scale gracefully, indicating the importance of the noise model.

Original languageEnglish (US)
Title of host publicationParallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings
EditorsEmma Hart, Ben Paechter, Julia Handl, Manuel López-Ibáñez, Peter R. Lewis, Gabriela Ochoa
PublisherSpringer- Verlag
Pages761-770
Number of pages10
ISBN (Print)9783319458229
DOIs
StatePublished - Jan 1 2016
Externally publishedYes
Event14th International Conference on Parallel Problem Solving from Nature, PPSN 2016 - Edinburgh, United Kingdom
Duration: Sep 17 2016Sep 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9921 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Parallel Problem Solving from Nature, PPSN 2016
CountryUnited Kingdom
CityEdinburgh
Period9/17/169/21/16

Keywords

  • Evolutionary algorithm
  • Noisy fitness
  • Theory

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