Evolving the structure of hidden Markov Models
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Evolving the structure of hidden Markov Models. / won, K. J.; Prugel-Bennett, A.; Krogh, A.
In: IEEE Transactions on Evolutionary Computation, Vol. 10, No. 1, 2006, p. 39-49.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Evolving the structure of hidden Markov Models
AU - won, K. J.
AU - Prugel-Bennett, A.
AU - Krogh, A.
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
U2 - 10.1109/TEVC.2005.851271
DO - 10.1109/TEVC.2005.851271
M3 - Journal article
VL - 10
SP - 39
EP - 49
JO - I E E E Transactions on Evolutionary Computation
JF - I E E E Transactions on Evolutionary Computation
SN - 1089-778X
IS - 1
ER -
ID: 1092465