Sequential metamodelling with genetic programming and particle swarms

Birkan Can; Cathal Heavey

doi:10.1109/WSC.2009.5429276

Sequential metamodelling with genetic programming and particle swarms

Birkan Can
, Cathal Heavey

School of Engineering

University of Limerick

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Proceedings of the 2009 Winter Simulation Conference, WSC 2009
Pages	3150-3157
Number of pages	8
DOIs	https://doi.org/10.1109/WSC.2009.5429276
Publication status	Published - 2009
Event	2009 Winter Simulation Conference, WSC 2009 - Austin, TX, United States Duration: 13 Dec 2009 → 16 Dec 2009

Publication series

Name	Proceedings - Winter Simulation Conference
ISSN (Print)	0891-7736

Conference

Conference	2009 Winter Simulation Conference, WSC 2009
Country/Territory	United States
City	Austin, TX
Period	13/12/09 → 16/12/09

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1109/WSC.2009.5429276

Cite this

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title = "Sequential metamodelling with genetic programming and particle swarms",

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.",

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AU - Heavey, Cathal

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AB - 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.

UR - https://www.scopus.com/pages/publications/77951644647

U2 - 10.1109/WSC.2009.5429276

DO - 10.1109/WSC.2009.5429276

M3 - Conference contribution

AN - SCOPUS:77951644647

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BT - Proceedings of the 2009 Winter Simulation Conference, WSC 2009

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Y2 - 13 December 2009 through 16 December 2009

ER -

Sequential metamodelling with genetic programming and particle swarms

Abstract

Publication series

Conference

UN SDGs

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