A Hybrid Genetic Algorithm for 2D FCC Hydrophobic-Hydrophilic Lattice Model to Predict Protein Folding

  • Md Tamjidul Hoque
  • Madhu Chetty
  • Laurence S. Dooley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


This paper presents a Hybrid Genetic Algorithm (HGA) for the protein folding prediction (PFP) applications using the 2D face-centred-cube (FCC) Hydrophobic-Hydrophilic (HP) lattice model. This approach enhances the optimal core formation concept and develops effective and efficient strategies to implement generalized short pull moves to embed highly probable short motifs or building blocks and hence forms the hybridized GA for FCC model. Building blocks containing Hydrophobic (H) – Hydrophilic (P or Polar) covalent bonds are utilized such a way as to help form a core that maximizes the |fitness|. The HGA helps overcome the ineffective crossover and mutation operations that traditionally lead to the stuck condition, especially when the core becomes compact. PFP has been strategically translated into a multi-objective optimization problem and implemented using a swing function, with the HGA providing improved performance in the 2D FCC model compared with the Simple GA.


Lattice Model Hybrid Genetic Algorithm Simple Genetic Algorithm Cube Model Topological Neighboring 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Md Tamjidul Hoque
    • 1
  • Madhu Chetty
    • 1
  • Laurence S. Dooley
    • 1
  1. 1.Gippsland School of Information TechnologyMonash UniversityChurchillAustralia

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