On the detection of general problem structures by using inductive linkage identification

Yuan Wei Huang*, Ying-Ping Chen

*Corresponding author for this work

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

2 Scopus citations

Abstract

Genetic algorithms and the descendant methods have been deemed robust and practical. To enhance the capabilities of genetic algorithms, tremendous effort has been invested in the field of evolutionary computation. One of the major trends to enhance genetic algorithms is to extract and exploit the relationship among variables, such as estimation of distribution algorithms and perturbation-based methods. In this study, we make an attempt to enable inductive linkage identification (ILI) to detect general problem structures, in which one variable may link to an arbitrary number of other variables. Our results indicate that the proposed technique can successfully detect the given problem structure.

Original languageEnglish
Title of host publicationProceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Pages1853-1854
Number of pages2
DOIs
StatePublished - 31 Dec 2009
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
CountryCanada
CityMontreal, QC
Period8/07/0912/07/09

Keywords

  • Evolutionary computation
  • Genetic algorithm
  • ILI
  • Inductive linkage identification
  • Linkage learning
  • Non-overlapping building block
  • Overlapping building block
  • Perturbation-based method
  • Problem decomposition
  • Problem structure

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