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Multi-objective evolutionary search strategies in constraint programming

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  • Bennetto, Robert
  • Vuuren, Jan H van

Abstract

It has been shown that evolutionary algorithms are able to construct suitable search strategies for classes of Constraint Satisfaction Problems (CSPs) in Constraint Programming. This paper is an explanation of the use of multi-objective optimisation in contrast to simple additive weighting techniques with a view to develop search strategies to classes of CSPs. A hierarchical scheme is employed to select a candidate strategy from the Pareto frontier for final evaluation. The results demonstrate that multi-objective optimisation significantly outperforms the single objective scheme in the same number of objective evaluations. In situations where strategies developed for a class of problems fail to extend to unseen problem instances of the same class, it is found that the structure of the underlying CSPs do not resemble those employed in the training process.

Suggested Citation

  • Bennetto, Robert & Vuuren, Jan H van, 2021. "Multi-objective evolutionary search strategies in constraint programming," Operations Research Perspectives, Elsevier, vol. 8(C).
    • Handle: RePEc:eee:oprepe:v:8:y:2021:i:c:s2214716020300671
    DOI: 10.1016/j.orp.2020.100177
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    References listed on IDEAS

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    1. Stewart, TJ, 1992. "A critical survey on the status of multiple criteria decision making theory and practice," Omega, Elsevier, vol. 20(5-6), pages 569-586.
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