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Estimation of Distribution Algorithms (EDAs) and Innovation Method arerecognized methods for solving global optimization problems and for theestimation of parameters in diffusion processes, respectively. Well known isalso that the quality of the Innovation Estimator strongly depends on anadequate selection of the initial value for the parameters when a localoptimization algorithm is used in its computation. Alternatively, in thispaper, we study the feasibility of a specific EDA - a continuous version of theUnivariate Marginal Distribution Algorithm (UMDAc) - for the computation of theInnovation Estimators. Numerical experiments are performed for two differentmodels with a high level of complexity. The numerical simulations show that theconsidered global optimization algorithms substantially improves theeffectiveness of the Innovation Estimators for different types of diffusionprocesses with complex nonlinear and stochastic dynamics.

An estimation of distribution algorithm for the computation of innovation estimators of diffusion processes