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Evolutionary algorithms and dynamic programming

  • Benjamin Doerr
  • , Anton Eremeev
  • , Christian Horoba
  • , Frank Neumann
  • , Madeleine Theile
  • Max-Planck-Institut fur Informatik
  • Sobolev Institute of Mathematics of the Siberian Branch of the Russian Academy of Sciences
  • University of Dortmund
  • TU Berlin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation, which enables them to construct solutions in a dynamic programming fashion. We take a general approach and relate the construction of such algorithms to the development of algorithms using dynamic programming techniques. Thereby, we give general guidelines on how to develop evolutionary algorithms that have the additional ability of carrying out dynamic programming steps.

Original languageEnglish
Title of host publicationProceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Pages771-777
Number of pages7
DOIs
Publication statusPublished - 31 Dec 2009
Externally publishedYes
Event11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 - Montreal, QC, Canada
Duration: 8 Jul 200912 Jul 2009

Publication series

NameProceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Conference

Conference11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Country/TerritoryCanada
CityMontreal, QC
Period8/07/0912/07/09

Keywords

  • Combinatorial optimization
  • Dynamic programming
  • Evolutionary algorithms

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