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  • A local search embedded genetic algorithm for simplified protein structure prediction

    Author(s)
    Rashid, Mahmood A
    Newton, MA Hakim
    Hoque, Md Tamjidul
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Rashid, Mahmood A.
    Newton, MAHakim A.
    Hoque, Md T.
    Year published
    2013
    Metadata
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    Abstract
    No single algorithm suits the best for the protein structure prediction problem. Therefore, researchers have tried hybrid techniques to mix the power of different strategies to gain improvements. In this paper, we present a hybrid search framework that embeds a tabu-based local search within a population based genetic algorithm. We applied our hybrid algorithm on simplified protein structure prediction problem. We use a low-resolution ab initio search method with the hydrophobic-polar energy model and face-centred-cubic lattice. Within the genetic algorithm, we apply local search in two different situations: i) only once at ...
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    No single algorithm suits the best for the protein structure prediction problem. Therefore, researchers have tried hybrid techniques to mix the power of different strategies to gain improvements. In this paper, we present a hybrid search framework that embeds a tabu-based local search within a population based genetic algorithm. We applied our hybrid algorithm on simplified protein structure prediction problem. We use a low-resolution ab initio search method with the hydrophobic-polar energy model and face-centred-cubic lattice. Within the genetic algorithm, we apply local search in two different situations: i) only once at the beginning and ii) every time at search stagnation. At the beginning, we apply local search to improve the randomly generated individuals and use them as an initial population for the genetic algorithm. Later, we apply local search after applying a random-walk at situations where the genetic algorithm gets stuck. In both cases, the use of local search is to improve the randomised solutions quickly. We experimentally show that our hybrid approach outperforms the state-of-the-art approaches.
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    Conference Title
    2013 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
    Publisher URI
    http://www.cec2013.org/
    DOI
    https://doi.org/10.1109/CEC.2013.6557688
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/58774
    Collection
    • Conference outputs

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