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Evolving the Structure of Hidden Markov Models

Record type: Article

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 optimisation 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 hand-crafted model that has been published in the literature.

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Citation

Won, Kyoung-Jae, Prügel-Bennett, Adam and Krogh, Anders (2006) Evolving the Structure of Hidden Markov Models IEEE Transactions on Evolutionary Computation, 10, (1), pp. 39-49.

More information

Published date: 2006
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 263992
URI: http://eprints.soton.ac.uk/id/eprint/263992
PURE UUID: a8159243-0169-4aa3-9e13-f61a124f380a

Catalogue record

Date deposited: 08 May 2007
Last modified: 18 Jul 2017 07:40

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Contributors

Author: Kyoung-Jae Won
Author: Adam Prügel-Bennett
Author: Anders Krogh

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