Memetic Elitist Pareto Evolutionary Algorithm of Three-Term Backpropagation Network for Classification Problems
نویسنده
چکیده
Evolutionary Algorithms (EAs) are population based algorithms, which allow for simultaneous exploration of different parts in the Pareto optimal set. This paper presents Memetic Elitist Pareto Evolutionary Algorithm of Three-Term Backpropagation Network for Classification Problems. This memetic elitist Pareto evolutionary algorithm is called METBP and used to evolve Three-term Backpropagation (TBP) network, which are optimal with respect to connection weight, error rates and architecture complexity simultaneously. METPB is based on NSGA-II benefit from the local search algorithm that used to enhance the individuals in the population of the algorithm. The numerical results of METPB show the advantages of the combination of the local search algorithm, and it is able to obtain a TBP network with better classification accuracy and simpler structure when compared with a multiobjective genetic algorithm based TBP network (MOGATBP) and some methods found in the literature, the results indicate that the proposed method is a potentially useful classifier for enhancing classification process ability.
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