Multi-objective evolutionary search strategies in constraint programming

• Published in: Operations Research Perspectives Vol. 8; pp. 1 - 15
• Main Authors: Bennetto, Robert, van Vuuren, Jan
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier 2021; Elsevier Ltd
• Subjects: Combinatorial optimization; Constraint programming; Genetic algorithms; Multi-objective optimization; Multi-objective optimization; Combinatorial optimization; Constraint programming; Genetic algorithms
• ISSN: 2214-7160; 2214-7160
• DOI: 10.1016/j.orp.2020.100177

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.