The evolutionary computation approach to motif discovery in biological sequences
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Intentional motion on-line learning and prediction
Machine Vision and Applications
HMM parameter estimation with genetic algorithm for handwritten word recognition
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Capturing dynamics on multiple time scales: a hybrid approach for cluttered electromagnetic data
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Evolving fisher kernels for biological sequence classification
Evolutionary Computation
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Summary: Hidden Markov models (HMMs) are widely used for biological sequence analysis because of their ability to incorporate biological information in their structure. An automatic means of optimizing the structure of HMMs would be highly desirable. However, this raises two important issues; first, the new HMMs should be biologically interpretable, and second, we need to control the complexity of the HMM so that it has good generalization performance on unseen sequences. In this paper, we explore the possibility of using a genetic algorithm (GA) for optimizing the HMM structure. GAs are sufficiently flexible to allow incorporation of other techniques such as Baum--Welch training within their evolutionary cycle. Furthermore, operators that alter the structure of HMMs can be designed to favour interpretable and simple structures. In this paper, a training strategy using GAs is proposed, and it is tested on finding HMM structures for the promoter and coding region of the bacterium Campylobacter jejuni. The proposed GA for hidden Markov models (GA-HMM) allows, HMMs with different numbers of states to evolve. To prevent over-fitting, a separate dataset is used for comparing the performance of the HMMs to that used for the Baum--Welch training. The GA-HMM was capable of finding an HMM comparable to a hand-coded HMM designed for the same task, which has been published previously.