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The cross entropy (CE) method is a model based search method to solveoptimization problems where the objective function has minimal structure. TheMonte-Carlo version of the CE method employs the naive sample averagingtechnique which is inefficient, both computationally and space wise. We providea novel stochastic approximation version of the CE method, where the sampleaveraging is replaced with incremental geometric averaging. This approach cansave considerable computational and storage costs. Our algorithm is incrementalin nature and possesses additional attractive features such as accuracy,stability, robustness and convergence to the global optimum for a particularclass of objective functions. We evaluate the algorithm on a variety of globaloptimization benchmark problems and the results obtained corroborate ourtheoretical findings.

A Cross Entropy based Optimization Algorithm with Global Convergence Guarantees