A genetic algorithm hybrid for constructing optimal response surface designs

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

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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 languageEnglish (US)
Pages (from-to)637-650
Number of pages14
JournalQuality and Reliability Engineering International
Issue number7
StatePublished - Nov 2004


  • 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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