Abstract
Memetic algorithms arise as very effective algorithms to obtain reliable and high accurate solutions for complex continuous optimization problems. Nowadays, high-dimensional optimization problems are an interesting field of research. Its high dimension introduces new problems for the optimization process, making recommendable to test the behavior of optimization algorithms to large-scale problems. In memetic algorithms, the local search method is responsible of exploring the neighborhood of the current solutions; therefore, the dimensionality has a direct influence over this component. The aim of this paper is to study this influence. We design different memetic algorithms that only differ in the local search method applied, and they are compared using two sets of continuous benchmark functions: a standard one and a specific set with large-scale problems. The results show that high dimensionality reduces the differences among the different local search methods.
Published Version
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