A new co-evolutionary algorithm based on constraint decomposition

Handling constraints is not a trivial task in evolutionary computing. Even if different techniques have been proposed in the literature, very few have considered co-evolution which tends to decompose problems into easier sub-problems. Existing co-evolutionary approaches have been mainly used to separate the decision vector. In this article we propose a different co-evolutionary approach, referred to as co-evolutionary constraint decomposition algorithm (CCDA), that relies on a decomposition of the constraints. Indeed, it is generally the conjunction of some specific constraints which hardens the problems. The proposed CCDA generates one subpopulation for each constraint and optimizes its own local fitness. A sub-population will first try to satisfy its assigned constraint, then the remaining constraints from other subpopulations using a cooperative mechanism, and finally the original objective function. Thanks to this approach, subpopulations will have different behaviors and solutions will approach the feasible domain from different sides. An exchange of information is performed using crossover between individuals from different sub-populations while mutation is applied locally. Promising mutated features are then transmitted through mating. The proposed CCDA has been validated on 8 wellknown benchmarks from the literature. Experimental results show the relevance of constraint decomposition in the context of co-evolution compared to state-of-the-art algorithms.