A complete and effective move set for simplified protein folding
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Genetic algorithms with local search optimization for protein structure prediction problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Protein Structure Prediction Using Evolutionary Algorithms Hybridized with Backtracking
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
An efficient hybrid Taguchi-genetic algorithm for protein folding simulation
Expert Systems with Applications: An International Journal
An enhanced genetic algorithm for protein structure prediction using the 2d hydrophobic-polar model
EA'05 Proceedings of the 7th international conference on Artificial Evolution
Protein Folding in Simplified Models With Estimation of Distribution Algorithms
IEEE Transactions on Evolutionary Computation
Multiobjectivizing the HP model for protein structure prediction
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
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The problem of predicting a protein structure with minimum energy from a sequence of amino acids has a significant importance in biology. This is a computationally challenging problem being NP-hard even in simplified lattice protein models such as the hydrophobic-polar (HP) model. We investigate the performance of hybrid evolutionary algorithms in the context of the bidimensional HP model. A new fitness function to evaluate the quality of a protein conformation is proposed and engaged in a hybrid evolutionary model to address protein structure prediction. The evolutionary model relies on hill-climbing strategies integrated in the search operators and a meaningful diversification of genetic material. The proposed fitness takes into account the energy of the conformation as well as the existence of a certain conformation H core on the HP lattice. The resulting weighted fitness function is able to guide the evolutionary search in a more efficient way as emphasized by computational experiments.