I. Introduction
Modern stochastic algorithms such as evolutionary algorithms (EA) draw inspiration from biological evolution. EAs, unlike conventional numerical optimization methods, produce new search points that do not use information about the local slope of the objective function and are thus not prone to stalling at local optima. Instead they involve a search from a “population” of solutions; making use of competitive selection, recombination and mutation operators to generate new solutions which are biased towards better regions of the search space. Further, they have shown considerable potentials for solving optimization problems that are characterized by non-convex, disjoint or noisy solution spaces. Modern stochastic optimizers, which have attracted a great deal of attention in recent years; include simulated annealing, tabu search, genetic algorithms, evolutionary programming, evolutionary strategies, differential evolution and many others [1], [2], [3] and [4]. These stochastic methods have been successfully applied to many real world optimization problems.