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Efficient Computation of Fitness Function by Pruning in Hydrophobic-Hydrophilic Model

  • Md. Tamjidul Hoque
  • Madhu Chetty
  • Laurence S. Dooley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)

Abstract

The use of Genetic Algorithms in a 2D Hydrophobic-Hydrophilic (HP) model in protein folding prediction application requires frequent fitness function computations. While the fitness computation is linear, the overhead incurred is significant with respect to the protein folding prediction problem. Any reduction in the computational cost will therefore assist in more efficiently searching the enormous solution space for protein folding prediction. This paper proposes a novel pruning strategy that exploits the inherent properties of the HP model and guarantee reduction of the computational complexity during an ordered traversal of the amino acid chain sequences for fitness computation, truncating the sequence by at least one residue.

Keywords

Fitness Function Monte Carlo Hydrophobic Residue Fitness Computation Pruning Strategy 
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 2005

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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