Incremental evolution of local search heuristics

  • Dara Curran
  • , Eugene Freuder
  • , Thomas Jansen

Research output: Chapter in Book/Report/Conference proceedingsConference proceedingpeer-review

Abstract

In evolutionary computation, incremental evolution refers to the process of employing an evolutionary environment that becomes increasingly complex over time. We present an implementation of this approach to develop randomised local search heuristics for constraint satisfaction problems, combining research on incremental evolution with local search heuristics evolution. A population of local search heuristics is evolved using a genetic programming framework on a simple problem for a short period and is then allowed to evolve on a more complex problem. Experiments compare the performance of this population with that of a randomly initialised population evolving directly on the more complex problem. The results obtained show that incremental evolution can represent a significant improvement in terms of optimisation speed, solution quality and solution structure.

Original languageEnglish
Title of host publicationProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10
Pages981-982
Number of pages2
DOIs
Publication statusPublished - 2010
Event12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010 - Portland, OR, United States
Duration: 7 Jul 201011 Jul 2010

Publication series

NameProceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Conference

Conference12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
Country/TerritoryUnited States
CityPortland, OR
Period7/07/1011/07/10

Keywords

  • Genetic programming
  • Graph colouring
  • Incremental evolution
  • Local search heuristics

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