Improving Artificial Neural Networks Based on Hybrid Genetic Algorithms

Artificial neural network (ANN) has outstanding characteristics in machine learning, fault, tolerant, parallel reasoning and processing nonlinear problem abilities. But BP training algorithm is based on the error gradient descent mechanism that the weight inevitably fall into the local minimum points. In this paper, a hybrid genetic algorithms(HGA) was proposed to solve the problem. The proposed HGA incorporates simulated annealing into a basic genetic algorithm that enables the algorithm to perform genetic search over the subspace of local optima. The two proposed solution methods were compared on Shaffer function global optimal problems, and the results indicated that HGA was successful in evolving ANNs.