Hybrid NSGA-II of Three-Term Backpropagation network for multiclass classification problems

Hybridization has become one of the current focuses of new research areas of the evolutionary algorithms over the past few years. Hybridization offers better speed of convergence to the evolutionary approach and better accuracy of the final solutions. This paper presents a hybrid non-dominated sorting genetic algorithm-II (NSGA-II) to optimize Three-Term Backpropagation (TBP) network in terms of two objectives which are: accuracy and complexity of the network. Backpropagation algorithm (BP) is often used as a local search algorithm and when combined with NSGA-II, the performance of NSGA II is enhanced due to the improvement of the individuals in the population. The experimental results show that the proposed method is effective in multiclass classification problems. The results of the hybrid approach to the classification problems are compared with multiobjective genetic algorithm based TBP network (MOGATBP) and some methods found in the literature. Moreover, the results indicate that the proposed method is a potentially useful classifier for enhancing classification process ability.