Fernández-Villacañas, José Luis3, Guervós, Juan Julián Merelo3, Schwefel, Hans-Poul3, Beyer, Hans-George3, Adamidis, Panagiotis3
1 Department of Computer Science, Faculty of Science, Aarhus University, Aarhus University2 Bioinformatics Research Centre (BiRC), Faculty of Science, Aarhus University, Aarhus University3 unknown
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.
Proceedings of the 7th International Conference on Parallel Problem Solving From Nature: Parallel Problem Solving From Nature --- Ppsn Vii, 2002, p. 861-870
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International Conference on Parallel Problem Solving from Nature, 2002