Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (1143 - 1198) " Distributed Bioinspired Algorithms"; Guest editors: F. Fernández de Vega, E. Cantú-Paz
Parallel Protein Structure Prediction by Multiobjective Optimization
PDP '09 Proceedings of the 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing
Robust Bio-active Peptide Prediction Using Multi-objective Optimization
BIOSCIENCESWORLD '10 Proceedings of the 2010 International Conference on Biosciences
Guiding the Search for Native-like Protein Conformations with an Ab-initio Tree-based Exploration
International Journal of Robotics Research
On discrete models and immunological algorithms for protein structure prediction
Natural Computing: an international journal
Comparison of parallel multi-objective approaches to protein structure prediction
The Journal of Supercomputing
Populating Local Minima in the Protein Conformational Space
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
An evolutionary model based on hill-climbing search operators for protein structure prediction
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Locality-based multiobjectivization for the HP model of protein structure prediction
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Efficient basin hopping in the protein energy surface
BIBM '12 Proceedings of the 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Ab-initio structure prediction refers to the problem of using only knowledge of the sequence of amino acids in a protein molecule to find spatial arrangements, or conformations, of the amino-acid chain capturing the protein in its biologically-active or native state. This problem is a central challenge in computational biology. It can be posed as an optimization problem, but current top ab-initio protocols employ Monte Carlo sampling rather than evolutionary algorithms (EAs) for conformational search. This paper presents a hybrid EA that incorporates successful strategies used in state-of-the-art ab-initio protocols. Comparison to a top Monte-Carlo-based sampling method shows that the domain-specific enhancements make the proposed hybrid EA competitive. A detailed analysis on the role of crossover operators and a novel implementation of homologous 1-point crossover shows that the use of crossover with mutation is more effective than mutation alone in navigating the protein energy surface.