Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Optimizing Hidden Markov Models with a Genetic Algorithm
AE '95 Selected Papers from the European conference on Artificial Evolution
Analysis of parallel genetic algorithms on HMM based speech recognition system
IEEE Transactions on Consumer Electronics
The evolutionary computation approach to motif discovery in biological sequences
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
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
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Information Sciences: an International Journal
A novel optimization of profile HMM by a hybrid genetic algorithm
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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Hidden Markov models (HMM) are widely used for speech recognition and have recently gained a lot of attention in the bioinformatics community, because of their ability to capture the information buried in biological sequences. Usually, heuristic algorithms such as Baum-Welch are used to estimate the model parameters. However, Baum-Welch has a tendency to stagnate on local optima. Furthermore, designing an optimal HMM topology usually requires a priori knowledge from a field expert and is usually found by trial-and-error. In this study, we present an evolutionary algorithm capable of evolving both the topology and the model parameters of HMMs. The applicability of the method is exemplified on a secondary structure prediction problem.