Authors: Hengyun Lu, Genke Yang, Lam Fat Yeung
Addresses: Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China. ' Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China. ' Department of Electronic Engineering, City University, Tat Chee Ave., Kowloon, Hong Kong
Abstract: This paper presents a novel search strategy for protein folding by combining genetic algorithm (GA) with extremal optimisation (EO). In the proposed algorithm, a constrained structure is proposed to reduce the complexity of algorithm. EO quickly approaches near-optimal solutions and GA generates an improved generation of global approximations. We demonstrate that the marriage of GA and EO can be applied successfully to the protein folding problem. The results show that the algorithm can find these best solutions so far for the listed benchmarks. Within the achieved results, the search converged rapidly and efficiently.
Keywords: protein folding; genetic algorithms; extremal optimisation; search strategy.
International Journal of Modelling, Identification and Control, 2010 Vol.10 No.1/2, pp.66 - 71
Available online: 02 Jul 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article