Evolving the structure of hidden Markov models

Authors:
Kyoung-Jae Won;A. Prugel-Bennett;A. Krogh
Affiliations:
Sch. of Electron. & Comput. Sci., Univ. of Southampton, UK;-;-
Venue:
IEEE Transactions on Evolutionary Computation
Year:
2006

Citing 0
Cited 3

Forecasting Approach Using Hybrid Model ASVR/NGARCH with Quantum Minimization

ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Automatic selection of Pareto-optimal topologies of hidden Markov models using multicriteria evolutionary algorithms

EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Evolving fisher kernels for biological sequence classification

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.