Mixture conditional estimation using genetic algorithms

Abstract

There are several methods for analyzing and estimating parameters for mixture models. These approaches seek to optimize various aspects of mixture model estimation, such as accuracy and computation cost. We present a new approach for estimating parameters of a Gaussian mixture model by genetic algorithms (GA). GA are adaptive search techniques designed to find near-optimal solutions of large-scale optimization problems with multiple local maxima. It is shown that using GA can find mixture model parameters accurately and efficiently for noisy and noiseless data sets.