A general algorithm scheme mixing computational intelligence with Bayesian simulation

In this paper, a general algorithm scheme which mixes computational intelligence with Bayesian simulation is proposed. This hybridization retains the advantage of computational intelligence in searching optimal point and the ability of Bayesian simulation in drawing random samples from any arbitrary probability density. An adaptive importance sampling (IS) method is developed under this framework, and the objective is to obtain a feasible mixture approximation to a multivariate, multi-modal and peaky target density, which can only be evaluated pointwise up to an unknown constant. The parameter of the IS proposal is determined with the aid of simulated annealing as well as some heuristics. The performance of this algorithm is compared with a counterpart algorithm that doesn´t involve any kind of computational intelligence. The result shows a remarkable performance gain due to the mixture strategy and so gives proof-of-concept of the proposed scheme.