Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Protein folding in the hydrophobic-hydrophilic (HP) is NP-complete
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Simple Analytical Models of Genetic Algorithms for Multimodal Function Optimization
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithm for 3D Protein Folding Simulations
Proceedings of the 5th International Conference on Genetic Algorithms
A Standard GA Approach to Native Protein Conformation Prediction
Proceedings of the 6th International Conference on Genetic Algorithms
Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Computational protein folding: from lattice to all-atom
IBM Systems Journal - Deep computing for the life sciences
Handbook of Computational Molecular Biology (Chapman & All/Crc Computer and Information Science Series)
An evolutionary algorithm with species-specific explosion for multimodal optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Effect of spatial locality on an evolutionary algorithm for multimodal optimization
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
An Immune Algorithm for Protein Structure Prediction on Lattice Models
IEEE Transactions on Evolutionary Computation
Protein Folding in Simplified Models With Estimation of Distribution Algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary multimodal optimization using the principle of locality
Information Sciences: an International Journal
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This paper considers the protein structure prediction problem as a multimodal optimization problem. In particular, de novo protein structure prediction problems on the 3D Hydrophobic-Polar (HP) lattice model are tackled by evolutionary algorithms using multimodal optimization techniques. In addition, a new mutation approach and performance metric are proposed for the problem. The experimental results indicate that the proposed algorithms are more effective than the state-of-the-arts algorithms, even though they are simple.