Evolving the structure of hidden Markov Models
A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C jejuni. The resulting HMM has a superior discrimination ability to a handcrafted model that has been published in the literature.
genetic algorithm (ga) hidden markov model (hmm) biological sequence analysis hybrid algorithm machine learning