An estimation of distribution algorithm based on nonparametric density estimation

Probabilistic models play a key role in an estimation of distribution algorithm(EDA). Generally, the form of a probabilistic model has to be chosen before executing an EDA. In each generation, the probabilistic model parameters will be estimated by training the model on a set of selected individuals and new individuals are then sampled from the probabilistic model. In this paper, we propose to use probabilistic models in a different way: firstly generate a set of candidate points, then find some as offspring solutions by a filter which is based on a nonparametric density estimation method. Based on this idea, we propose a nonparametric estimation of distribution algorithm (nEDA) for global optimization. The major differences between nEDA and traditional EDAs are (1) nEDA uses a generating filtering strategy to create new solutions while traditional EDAs use a model building-sampling strategy to generate solutions, and (2) nEDA utilizes a nonparametric density model with traditional EDAs usually utilize parametric density models. nEDA is compared with a traditional EDA which is based on Gaussian model on a set of benchmark problems. The preliminary experimental results show that nEDA is promising for dealing with global optimization problems.