A New Genetic Algorithm for Simplified Protein Structure Prediction
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In this paper, we present a new genetic algorithm for protein structure prediction problem using face-centred cubic lattice and hydrophobic-polar energy model. Our algorithm uses i) an exhaustive generation approach to diversify the search; ii) a novel hydrophobic core-directed macro move to intensify the search; and iii) a random-walk strategy to recover from stagnation. On a set of standard benchmark proteins, our algorithm significantly outperforms the state-of-the-art algorithms for the same models.
25th Australasian Joint Conference on Artificial Intelligence, AI 2012
© 2012 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
Artificial Intelligence and Image Processing not elsewhere classified