Hard Test Generation for Maximum Flow Algorithms with the Fast Crossover-Based Evolutionary Algorithm

Vladimir Mironovich, Maxim Buzdalov

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Most evolutionary algorithms not only throw out insufficiently good solutions, but forget all information they obtained from their evaluation, which reduces their speed from the information theory point of view. An evolutionary algorithm which does not do that, the 1+(λ,β) EA was recently proposed by Doerr, Doerr and Ebel. We evaluate this algorithm on the problem of finding hard tests for maximum flow algorithms. Experiments show that the 1+(λ,β) EA is never the best, but is quite stable. However, its adaptive version, known for being superior for the OneMax problem, is shown to be one of the worst.
Original languageEnglish
Title of host publicationGECCO Companion '15
Subtitle of host publicationProceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
EditorsSara Silva
PublisherAssociation for Computing Machinery
Pages1229-1232
Number of pages4
ISBN (Print)978-1-4503-3488-4
DOIs
Publication statusPublished - 11 Jul 2015
Externally publishedYes
Event16th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015

Conference

Conference16th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/TerritorySpain
CityMadrid
Period11 Jul 201515 Jul 2015

Keywords

  • black-box complexity
  • evolutionary algorithms
  • test generation
  • worst-case execution time

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