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A Metaheuristic Approach to Protein Structure PredictionLandscape Characterization and Algorithms Selection for the PSP Problem

A Metaheuristic Approach to Protein Structure Prediction: Landscape Characterization and... [Fitness landscape analysis (FLA) is a technique to determine the characteristics of a problem or its structural features based on which the most appropriate algorithm is possible to recommend for solving the problem. In this chapter, we determine structural features of the protein structure prediction problem by analyzing the landscape structure. A landscape of the protein instances is generated by using the quasi-random sampling technique and city block distance. Structural features of the PSP Landscape are determined by applying various landscape measures. Numerical results indicate that the complexity of the PSP problem increases with protein sequence length. Six well-known real-coded optimization algorithms are evaluated over the same set of protein sequences and the performances are subsequently analyzed based on the structural features. Finally, we suggest the most appropriate algorithm(s) for solving the PSP problem.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Metaheuristic Approach to Protein Structure PredictionLandscape Characterization and Algorithms Selection for the PSP Problem

Part of the Emergence, Complexity and Computation Book Series (volume 31)
Jana, Nanda Dulal; Das, Swagatam; Sil, Jaya
Springer Journals — Mar 3, 2018
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63 pages

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2018
ISBN
978-3-319-74774-3
Pages
87 –150
DOI
10.1007/978-3-319-74775-0_4
Publisher site
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Abstract

[Fitness landscape analysis (FLA) is a technique to determine the characteristics of a problem or its structural features based on which the most appropriate algorithm is possible to recommend for solving the problem. In this chapter, we determine structural features of the protein structure prediction problem by analyzing the landscape structure. A landscape of the protein instances is generated by using the quasi-random sampling technique and city block distance. Structural features of the PSP Landscape are determined by applying various landscape measures. Numerical results indicate that the complexity of the PSP problem increases with protein sequence length. Six well-known real-coded optimization algorithms are evaluated over the same set of protein sequences and the performances are subsequently analyzed based on the structural features. Finally, we suggest the most appropriate algorithm(s) for solving the PSP problem.]

Published: Mar 3, 2018

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