Optimum Design Using Radial Basis Function Network and Adaptive Range Genetic Algorithms. 1st Report. Consideration in Unconstrained Optimization.

Abstract

In this paper, we use the radial basis function network in order to approximate the fitness function of the genetic algorithms and try to obtain the approximate optimum results within the relatively small number of function call. The radial basis function networks (RBF) is a kind of neural network that is composed by the number of radial basis function in Gaussian distribution. RBF has learning system that is composed by additional learning of a basis function and a new data and forgetting of a basis function and an undesirable data. Thus the key issues in RBF are to give new data and to place basis function. So that if we can give these values appropriately, we can carry out approximate optimization even in the case that the optimum solutions are outside the range of the initial settings. Together with the adaptive range genetic algorithms that are proposed to treat mixed variable optimization, we will propose the way to give a new data and basis function. In this study, we have shown the effectiveness of the proposed method through simple numerical examples in unconstrained optimum design case.