Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction

Houqe, Tamjid; Chetty, Madhu and Dooley, Laurence S. (2007). Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction. In: Grosan, Crina; Abraham, Ajith and Ishibuchi, Hisao eds. Hybrid Evolutionary Algorithms. Studies in Computational Intelligence Series (SCI), 75. USA: Springer, pp. 235–256.

DOI: https://doi.org/10.1007/978-3-540-73297-6_10

Abstract

Protein folding prediction (PFP), especially the ab initio approach, is one of the most challenging problems facing the bioinformatics research community due to it being extremely complex to solve and computationally very intensive. Hybrid evolutionary computing techniques have assumed considerable importance in attempting to overcome these challenges and so this chapter explores some of these PFP issues. By using the well-known Hydrophobic–Hydrophilic (HP) model, the performance of a number of contemporary nondeterministic search techniques are examined. Particular emphasis is given to the new Hybrid Genetic Algorithm (HGA) approach, which is shown to provide a number of performance benefits for PFP applications.

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