Statistical Learning In Optimization: Gaussian Modeling For Population Search

Population search algorithms for optimization problems such as Genetic algorithm is an effective way to find an optimal value, especially when we have little information about the objective function. Baluja has proposed effective 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 defining 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. KEYWORDS: probabilistic and statistical methods, optimization, population search 1. INTRODUCTION When we want to optimize some complex function with a lot of local optima, random search (Monte Carlo) algorithms are effective to avoid converging to a non-global optimum. In order to generate good candidate solutio..