Sequential metamodelling with genetic programming and particle swarms

Birkan Can, Cathal Heavey

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

Abstract

This article presents an application of two main component methodologies of evolutionary algorithms in simulation-based metamodelling. We present an evolutionary framework for constructing analytical metamodels and apply it to simulations of manufacturing lines with buffer allocation problem. In this framework, a particle swarm algorithm is integrated to genetic programming to perform symbolic regression of the problem. The sampling data is sequentially generated by the particle swarm algorithm, while genetic programming evolves symbolic functions of the domain. The results are promising in terms of efficiency in design of experiments and accuracy in global metamodelling.

Original languageEnglish
Title of host publicationProceedings of the 2009 Winter Simulation Conference, WSC 2009
Pages3150-3157
Number of pages8
DOIs
Publication statusPublished - 2009
Event2009 Winter Simulation Conference, WSC 2009 - Austin, TX, United States
Duration: 13 Dec 200916 Dec 2009

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Conference

Conference2009 Winter Simulation Conference, WSC 2009
Country/TerritoryUnited States
CityAustin, TX
Period13/12/0916/12/09

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