Ant colony optimization beats resampling on noisy functions

Tobias Friedrich, Timo Kötzing, Francesco Quinzan, Andrew M. Sutton

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

Abstract

Despite the pervasiveness of noise in real-world optimization, there is little understanding of the interplay between the operators of randomized search heuristics and explicit noise-handling techniques such as statistical resampling. Ant Colony Optimization (ACO) algorithms are claimed to be particularly well-suited to dynamic and noisy problems, even without explicit noise-handling techniques. In this work, we empirically investigate the trade-offs between resampling an the noise-handling abilities of ACO algorithms. Our main focus is to locate the point where resampling costs more than it is worth.

Original languageEnglish (US)
Title of host publicationGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
EditorsTobias Friedrich
PublisherAssociation for Computing Machinery, Inc
Pages3-4
Number of pages2
ISBN (Electronic)9781450343237
DOIs
StatePublished - Jul 20 2016
Event2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States
Duration: Jul 20 2016Jul 24 2016

Publication series

NameGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

Other

Other2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
CountryUnited States
CityDenver
Period7/20/167/24/16

Bibliographical note

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • Ant Colony Optimization
  • Crossover
  • Estimation of Distribution Algorithm
  • Evolutionary Algorithm
  • Genetic Algorithm
  • Noise
  • Robustness

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