IDEAs based on the normal kernels probability density function

Abstract

The IDEA framework is a general framework for iterated density estimation evolutionary algorithms. These algorithms use probabilistic models to guide the search in stochastic optimization. The estimation of densities for subsets of selected samples and the sampling from the resulting distributions, is the combination of the evolutionary recombination and mutation steps used in EAs. We investigate how the normal kernels probability density function can be used as the distribution of the problem variables in order to perform optimization using the IDEA framework. As a result, we present three probability density structure search algorithms, as well as a general estimation and samphng algorithm, all of which use the normal kernels distribution