  The cross entropy (CE) method is a model based search method to solve
optimization problems where the objective function has minimal structure. The
Monte-Carlo version of the CE method employs the naive sample averaging
technique which is inefficient, both computationally and space wise. We provide
a novel stochastic approximation version of the CE method, where the sample
averaging is replaced with incremental geometric averaging. This approach can
save considerable computational and storage costs. Our algorithm is incremental
in nature and possesses additional attractive features such as accuracy,
stability, robustness and convergence to the global optimum for a particular
class of objective functions. We evaluate the algorithm on a variety of global
optimization benchmark problems and the results obtained corroborate our
theoretical findings.
