Statistical Learning in Optimization: Gaussian Modeling for Population Search
نویسنده
چکیده
Population search algorithms for optimization problems such as Genetic algorithm is an e ective way to nd an optimal value, especially when we have little information about the objective function. Baluja has proposed e ective algorithms modeling the distribution of elites explicitly by some statistical model. We propose such an algorithm based on Gaussian modeling of elites, and analyze the convergence property of the algorithm by de ning the objective function as a stochastic model. We point out that the algorithms based on the explicit modeling of the elites' distribution tend to converge to unpreferable local optima, and we modify the algorithm to conquer the defect.
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