An efficient hybrid Taguchi-genetic algorithm for protein folding simulation

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 to find the lowest energy conformation. In order to enhance the performance of predicting protein structure, in this paper we propose an efficient hybrid Taguchi-genetic algorithm that combines genetic algorithm, Taguchi method, and particle swarm optimization (PSO). The GA has the capability of powerful global exploration, while the Taguchi method can exploit the optimum offspring. In addition, we present the PSO inspired by a mutation mechanism in a genetic algorithm. We demonstrate that our algorithm can be applied successfully to the protein folding problem based on the hydrophobic-hydrophilic lattice model. Simulation results indicate that our approach performs very well against existing evolutionary algorithm.