An optimization method for neural network based on GA and TS algorithm
Pengyi Gao, Chuanbo Chen, Sheng Qin, Yingsong Hu · 2010
Although many global optimization search algorithms may be used to train feedforward neural networks, these algorithms have some weaknesses such as dependence of initial solution. This paper proposes a novel hybrid global optimization method for classification problem, called GTA, which combines the advantages of Genetic algorithm and Tabu search. The training process in proposed method is divided into two phase. First, a promising initial solution is searched by GA algorithm, and next the best solution is selected by tabu search. In this work, the optimization method and test are discussed. Results obtained by testing Diabetes Data Set have shown that the approach performs better than other optimization algorithm.