Abstract—The protein-folding problem (PFP) that is predicting the functional conformation of a protein from its amino acid sequence remains as a central problem in computational biology and it is a combinatorial optimization problem. Genetic algorithms (GA) have proved to be a successful method for predicting the protein structure. In this paper, we propose a novel hybrid genetic algorithm and we implement it for protein folding problem. In this approach, we simply allow the genetic algorithm to run to substantial convergence and then permit the local optimization procedure to take over. Genetic algorithm finds the hills and a more canonical method of local search; the Gradient like-bit wise (G-bit) improvement is used to climb the hill. We have demonstrated the superiority of our hybrid genetic algorithm for several instances of the protein-folding problem, which not only finds the optimum solution, but also finds them faster than the traditional genetic algorithms.
Index Terms—Evolutionary algorithms, G-bit improvement, Hybrid GA, protein structure prediction
M. V. Judy is with Amrita School of Arts and Sciences, Kochi, Kerala India. (email: firstname.lastname@example.org)
B. Ramadoss is with Master of Computer Application department, National Institute of technology, Trichy, India, (email@example.com)
Cite: M. V. Judy and B Ramadoss, "An Enhanced Solution to the Protein Folding Problem Using a Hybrid Genetic Algorithm with G-Bit Improvement Strategy," International Journal of Modeling and Optimization vol. 2, no. 3, pp. 356-359, 2012.