Optimizing hidden Markov models with a genetic algorithm

In this paper is presented the application of genetic algorithms (GAs) to the learning of hidden Markov models (HMMs). The Baum-Welch algorithm (BW): which optimizes the coefficients of a HMM, is improved by the use of a GA. The GA is able to find rapidly a good initial model compared to random generation, and this initial model is optimized further with BW. A representation and adapted genetic operators have been introduced in order to evolve matrix of probabilities. Several tests on artificial data show the interest in using a GA with BW.