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An Improved Multiobjectivization Strategy for HP Model-Based Protein Structure Prediction

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Parallel Problem Solving from Nature - PPSN XII (PPSN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7492))

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Abstract

Through multiobjectivization, a single-objective problem is restated in multiobjective form with the aim of enabling a more efficient search process. Recently, this transformation was applied with success to the hydrophobic-polar (HP) lattice model, which is an abstract representation of the protein structure prediction problem. The use of alternative multiobjective formulations of the problem has led to significantly better results. In this paper, an improved multiobjectivization for the HP model is proposed. By decomposing the HP model’s energy function, a two-objective formulation for the problem is defined. A comparative analysis reveals that the new proposed multiobjectivization evaluates favorably with respect to both the conventional single-objective and the previously reported multiobjective formulations. Statistical significance testing and the use of a large set of test cases support the findings of this study. Both two-dimensional and three-dimensional lattices are considered.

This research has been partially funded by CONACyT projects 105060 and 99276.

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Authors and Affiliations

  1. Information Technology Laboratory, CINVESTAV-Tamaulipas, Km. 5.5 carretera, Cd. Victoria-Soto La Marina. Cd. Victoria, Tamaulipas, 87130, México

    Mario Garza-Fabre, Eduardo Rodriguez-Tello & Gregorio Toscano-Pulido

Authors
  1. Mario Garza-Fabre
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  2. Eduardo Rodriguez-Tello
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  3. Gregorio Toscano-Pulido
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Editor information

Editors and Affiliations

  1. Departmento de Computation, Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Av. IPN No. 2508, Col. San Pedro Zacatenco, 0360, Mexico, D.F., Mexico

    Carlos A. Coello Coello

  2. Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, 95125, Catania, Italy

    Vincenzo Cutello  & Mario Pavone  & 

  3. Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology, Kanpur, Kanpur, India

    Kalyanmoy Deb

  4. Department of Computer Science, University of New Mexico, USA

    Stephanie Forrest

  5. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia

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Garza-Fabre, M., Rodriguez-Tello, E., Toscano-Pulido, G. (2012). An Improved Multiobjectivization Strategy for HP Model-Based Protein Structure Prediction. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_9

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  • DOI: https://doi.org/10.1007/978-3-642-32964-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32963-0

  • Online ISBN: 978-3-642-32964-7

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