Adaptive discretization for probabilistic model building genetic algorithms

Chao Hong Chen*, Wei Nan Liu, Ying-ping Chen

*Corresponding author for this work

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

13 Scopus citations

Abstract

This paper proposes an adaptive discretization method, called Split-on-Demand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization problems in the continuous domain. The procedure, effect, and usage of SoD are described in detail. As an example, the integration of SoD and the extended compact genetic algorithm (EGG A), named real-coded EGGA (rECGA), is presented and numerically examined. The experimental results indicate that rECGA works well and SoD is effective. The behavior of SoD is analyzed and discussed, followed by the potential future work for SoD.

Original languageEnglish
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
Pages1103-1110
Number of pages8
DOIs
StatePublished - 30 Oct 2006
Event8th Annual Genetic and Evolutionary Computation Conference 2006 - Seattle, WA, United States
Duration: 8 Jul 200612 Jul 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference
Volume2

Conference

Conference8th Annual Genetic and Evolutionary Computation Conference 2006
CountryUnited States
CitySeattle, WA
Period8/07/0612/07/06

Keywords

  • Adaptive discretization
  • Extended compact genetic algorithm
  • Real-parameter optimization
  • Split-on-demand

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    Chen, C. H., Liu, W. N., & Chen, Y. (2006). Adaptive discretization for probabilistic model building genetic algorithms. In GECCO 2006 - Genetic and Evolutionary Computation Conference (pp. 1103-1110). (GECCO 2006 - Genetic and Evolutionary Computation Conference; Vol. 2). https://doi.org/10.1145/1143997.1144174