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Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA

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Exploitation of Linkage Learning in Evolutionary Algorithms

Part of the book series: Evolutionary Learning and Optimization ((ALO,volume 3))

Abstract

Protein structure prediction (PSP) is one of the most important problems in computational biology. This chapter introduces a novel hybrid Estimation of Distribution Algorithm (EDA) to solve the PSP problem on HP model. Firstly, a composite fitness function containing the information of folding structure core (H-Core) is introduced to replace the traditional fitness function of HP model. The new fitness function is expected to select better individuals for probabilistic model of EDA. Secondly, local search with guided operators is utilized to refine found solutions for improving efficiency of EDA. Thirdly, an improved backtracking-based repairing method is introduced to repair invalid individuals sampled by the probabilistic model of EDA. It can significantly reduce the number of backtracking searching operation and the computational cost for long sequence protein. Experimental results demonstrate that the new method outperforms the basic EDAs method. At the same time, it is very competitive with other existing algorithms for the PSP problem on lattice HP models.

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Authors and Affiliations

  1. Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, 808-0135, Japan

    Benhui Chen & Jinglu Hu

Authors
  1. Benhui Chen
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  2. Jinglu Hu
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Editors and Affiliations

  1. Natural Computing Laboratory Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Road, 300, HsinChu City, Taiwan

    Ying-ping Chen

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Chen, B., Hu, J. (2010). Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA. In: Chen, Yp. (eds) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12834-9_9

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  • DOI: https://doi.org/10.1007/978-3-642-12834-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12833-2

  • Online ISBN: 978-3-642-12834-9

  • eBook Packages: EngineeringEngineering (R0)

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