Optimisation of density estimation models with evolutionary algorithms

Abstract

We propose a new optimisation method for estimating both the parameters and the structure, i. e. the number of components, of a finite mixture model for density estimation. We employ a hybrid method consisting of an evolutionary algorithm for structure optimisation in conjunction with a gradient-based method for evaluating each candidate model architecture. For structure modification we propose specific, problem dependent evolutionary operators. The introduction of a régularisation term prevents the models from over-fitting the data. Experiments show good generalisation abilities of the optimised structures.