Multiple sequence alignment using the hidden Markov model trained by an improved quantum-behaved particle swarm optimization

DOI10.1016/J.INS.2010.11.014zbMath1250.68239OpenAlexW2091384774MaRDI QIDQ713079

D. Kharzeev

Publication date: 26 October 2012

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2010.11.014

zbMATH Keywords

hidden Markov modelmultiple sequence alignmentparameter optimizationpopulation diversityquantum-behaved particle swarm optimization

Mathematics Subject Classification ID

Biochemistry, molecular biology (92C40) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Quantum algorithms and complexity in the theory of computing (68Q12)

Related Items (3)

Local path planning for unmanned surface vehicle collision avoidance based on modified quantum particle swarm optimization ⋮ A quantum-inspired vortex search algorithm with application to function optimization ⋮ A finite-time particle swarm optimization algorithm for odor source localization

Uses Software

• ClustalW
• Rose
• Pfam

Cites Work

• Complex system fault diagnosis based on a fuzzy robust wavelet support vector classifier and an adaptive Gaussian particle swarm optimization
• Swarm-based translation-invariant morphological prediction method for financial time series forecasting
• Multi-strategy ensemble particle swarm optimization for dynamic optimization
• Optimizing the codon usage of synthetic gene with QPSO algorithm
• Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients
• Using quantum-behaved particle swarm optimization algorithm to solve non-linear programming problems
• Online Learning with Hidden Markov Models
• A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
• Optimisation of HMM topology and its model parameters by genetic algorithms

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