摘要：Highlights•A multi-objective formulation is presented to solve for suitable search strategies in Constraint Programming.•It is shown that a multi-objective GP approach produces superior search strategies over the single objective equivalent.•Evolved search strategies are demonstrated to be exploit features of the underlying Contraint Satisfaction Problem graphs.•This paper presents a novel approach to searching for branching stratgies applied to classes of CSPs using a train and test methodology.AbstractIt has been shown that evolutionary algorithms are able to construct suitable search strategies for classes ofConstraint 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.
