A Standard GA Approach to Native Protein Conformation Prediction
Proceedings of the 6th International Conference on Genetic Algorithms
An Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
An efficient genetic algorithm for predicting protein tertiary structures in the 2D HP model
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Optimizing back-propagation networks via a calibrated heuristic algorithm with an orthogonal array
Expert Systems with Applications: An International Journal
Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neurofuzzy Systems
IEEE Transactions on Fuzzy Systems
Protein structure prediction in lattice models with particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Protein structure prediction using particle swarm optimization and a distributed parallel approach
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
Hybrid evolutionary algorithm with a composite fitness function for protein structure prediction
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Protein structure prediction using distributed parallel particle swarm optimization
Natural Computing: an international journal
| Hi-index | 12.05 |
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.