Sib-based survival selection technique for protein structure prediction in 3D-FCC lattice model

Abstract

Protein Structure Prediction (PSP) is a challenging optimization problem in computational biology. A large number of nondeterministic approaches such as Evolutionary Algorithms (EAs) have been have been effectively applied to a variety of fields though, in the rugged landscape of multimodal problem like PSP, it can perform unsatisfactorily, due to premature convergence. In EAs, selection plays a significant role to avoid getting trapped in local optima and also to guide the evolution towards an optimal solution. In this paper, we propose a new Sib-based survival selection strategy suitable for application in a genetic algorithm (GA) to deal with multimodal problems. The proposed strategy, inspired by the concept of crowding method, controls the flow of genetic material by pairing off the fittest offspring amongst all the sibs (offspring inheriting most of the genetic material from an ancestor) with its ancestor for survival. Furthermore, by selecting the survivors in a hybridized manner of deterministic and probabilistic selection, the method allows the exploitation of less fit solutions along with the fitter ones and thus facilitates escaping from local optima (minima in case of PSP). Experiments conducted on a set of widely used benchmark sequences for 3D-FCC HP lattice model, demonstrate the potential of the proposed method, both in terms of diversity and optimal energy in regard to various state-of-the-art selection methods.