A Multi-Objective Meta-Model Assisted Memetic Algorithm with Non Gradient-Based Local Search


In this paper, we present an approach in which a local search mechanism is coupled to a multi-objective evolutionary algorithm. The local search mechanism is assisted by a meta-model based on support vector machines. Such a mechanism consists of two phases: the first one involves the use of an aggregating function which is defined by different weighted vectors. For the (scalar) optimization task involved, we adopt a non-gradient mathematical programming technique: the Hooke-Jeeves method. The second phase computes new solutions departing from those obtained in the first phase. The local search engine generates a set of solutions which are used in the evolutionary process of our algorithm. The preliminary results indicate that our proposed approach is quite promising.