ABSTRACT
I. INTRODUCTION In many engineering disciplines a large spectrum of optimization problems has grown in size and complexity. In some instances, the solution to com plex multidimensional problems by using classicial optimization techniques is sometimes difficult and/or expensive. This realization has led to an increased interest in a special class of searching algorithm, namely, evolu tionary algorithms. In general, these are referred to as “stochastic” optimi zation techniques and their foundations lie in the evolutionary patterns observed in living things.
