Hybridization of evolutionary algorithms and local search by means of a clustering method

In this paper we present a hybrid evolutionary algorithm to solve nonlinear regression problems. Although evolutionary algorithms have proven their ability to explore large search spaces, they are comparatively inefficient in fine tuning the solution. This drawback is usually avoided by means of local optimization algorithms that are applied to the individuals of the population. The algorithms that use local optimization procedures are usually called hybrid algorithms. On the other hand, it is well known that the clustering process enables the creation of groups (clusters) with mutually close points that hopefully correspond to relevant regions of attraction. Then local search procedures can be started once in every such region. This work proposes the combination of an evolutionary algorithm, a clustering process and a local search procedure to the evolutionary design of product-units neural networks. In the methodology presented, only a few individuals are subject to local optimization. Moreover, the local optimization algorithm is only applied at specific stages of the evolutionary process. Our results show a favorable performance when the regression method proposed is compared to other standard methods