A genetic algorithm hybrid for constructing optimal response surface designs

David Drain; W. Matthew Carlyle; Douglas Montgomery; Connie Borror; Christine Anderson-Cook

doi:10.1002/qre.573

A genetic algorithm hybrid for constructing optimal response surface designs

David Drain, W. Matthew Carlyle, Douglas Montgomery, Connie Borror, Christine Anderson-Cook

Industrial, Systems and Operations Engineering

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

Hybrid heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective, and simple heuristics like simulated annealing fail to find good solutions. One such heuristic hybrid is GASA (genetic algorithm-simulated annealing), developed to take advantage of the exploratory power of the genetic algorithm, while utilizing the local optimum exploitive properties of simulated annealing. The successful application of this method is demonstrated in a difficult design problem with multiple optimization criteria in an irregularly shaped design region.

Original language	English (US)
Pages (from-to)	637-650
Number of pages	14
Journal	Quality and Reliability Engineering International
Volume	20
Issue number	7
DOIs	https://doi.org/10.1002/qre.573
State	Published - Nov 2004

Keywords

Design of experiments
Genetic algorithm
Heuristic optimization

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Management Science and Operations Research

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10.1002/qre.573

Cite this

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A genetic algorithm hybrid for constructing optimal response surface designs

Abstract

Keywords

ASJC Scopus subject areas

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