Integrating Genetic Algorithm and Neural Network to Optimize Statistical Problems

Many real word product or process design problems involve multi response problems which have stochastic nature. This paper proposes a hybrid approach involved genetic algorithm and artificial neural network methodology to solve these problems. Usually, in these problems the relationship between responses and independent variables is indeterminate; therefore to generate required input data we are interested to use a method to approximate this relationship. Artificial Neural Network (ANN) is a methodology employed in this research to evaluate linear and nonlinear relationship between variables. We model the statistical multi response problem by three different multi objective decision making (MODM) techniques. Moreover, fourdifferent genetic algorithms are proposed in which four pairwise multiple comparisons statistical tests are used to control the random nature of the problem. Finally, the performance of the proposed methodology is demonstrated using a tow way analysis of variance (ANOVA) for a numerical example and the results are compared statistically..

Keywords:

multi-response/optimization problems/Genetic algorithm , Neural/networks , Multi objective/decision making , Statistical pairwise multiple comparison