Conference Proceeding

Novel local improvement techniques in clustered memetic algorithm for protein structure prediction

GSIT, Monash Univ., Churchill, VIC, Australia
07/2011; DOI:10.1109/CEC.2011.5949727 pp.1003 - 1011 In proceeding of: Evolutionary Computation (CEC), 2011 IEEE Congress on
Source: IEEE Xplore

ABSTRACT Evolutionary algorithms (EAs) often fail to find the global optimum due to genetic drift. As the protein structure prediction problem is multimodal having several global optima, EAs empowered with combined application of local and global search e.g., memetic algorithms, can be more effective. This paper introduces two novel local improvement techniques for the clustered memetic algorithm to incorporate both problem specific and search-space specific knowledge to find one of the optimum structures of a hydrophobic-polar protein sequence on lattice models. Experimental results show the superiority of the proposed techniques against existing EAs on benchmark sequences.

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Keywords

benchmark sequences
 
clustered memetic algorithm
 
Evolutionary algorithms
 
global optima
 
hydrophobic-polar protein sequence
 
lattice models
 
memetic algorithms
 
optimum structures
 
paper introduces
 
problem specific
 
protein structure prediction problem
 
search-space specific knowledge
 
superiority