Abstract
Given the amino-acid sequence of a protein, the prediction of a protein's tertiary structure is known as the protein folding problem. The protein folding problem in the hydrophobic-hydrophilic lattice model is the problem of finding the lowest energy conformation. This is the NP-complete problem. In order to enhance the procedure performance for predicting protein structures, a hybrid genetic-based particle swarm optimization (PSO) is proposed. Simulation results indicate that our approach outperforms the existing evolutionary algorithms. The method can be applied successfully to the protein folding problem based on the three-dimensional hydrophobic- hydrophilic lattice model.
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