Hybrid evolutionary algorithms for constraint satisfaction problems: memetic overkill?

We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contribution of the evolutionary component to their performance by comparing the hybrid EAs with their "de-evolutionarised" variants. The experiments show that "de-evolutionarising" can increase performance, in some cases doubling it. Considering that the problem domain and the algorithms are arbitrarily selected from the "memetic niche", it seems likely that the same effect occurs for other problems and algorithms. Therefore, our conclusion is that after designing and building a memetic algorithm, one should perform a verification by comparing this algorithm with its "de-evolutionarised" variant.

1 Introduction

During the last decade, many researchers have adopted the use of heuristics within an evolutionary algorithm (EA) because of the positive effect on algorithm performance. Advocated already in the mid 90ies (cf. [22]), such algorithms, called hybrid EAs or memetic algorithms, offer the best of both words: the robustness of the EA because of the unbiased population-based search and the directed search implied by the heuristic bias. As for algorithm performance, it is assumed and expected that the hybrid EA performs better than the EA alone and the heuristic alone. Supported by significant practical evidence, the contemporary view within the EC community considers this memetic approach the most successful in treating challenging (combinatorial) optimisation problems.