Evolving the Structure of Hidden Markov Models

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.


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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.

Item Type: Article
Organisations: Southampton Wireless Group
ePrint ID: 263992
Date :
Date Event
Date Deposited: 08 May 2007
Last Modified: 17 Apr 2017 19:45
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/263992

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