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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(4): 329-333 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.529

Modified Off-lattice AB Model for Protein Folding Problem Using the Vortex Search Algorithm

Berat Doğan and Tamer Ölmez
Abstract—The energy function of the off-lattice AB model has a number of deep valleys and hills which usually leads the search algorithms to trap into a local minimum point. Existing studies usually performs algorithmic improvements on the well-known search methods to avoid from these local minimum points. However, these algorithmic improvements further increase the computational time which is not desired for the protein folding problem. In this study, it is aimed to smooth the energy landscape of this energy function and thus, to find a near optimal or optimal configuration without performing algorithmic improvements on the search methods. This is achieved by adding an additional term to the original energy function by which a hydrophobic core is formed and a near optimal and optimal configuration is found easily. In the experiments, a newly proposed optimization algorithm, the Vortex Search (VS) algorithm, is used to minimize both the original and modified energy functions. Experimental results showed that, the modified energy function helps the VS algorithm to find the desired configurations much more easier than the original function when the maximum number of iterations is kept equal for both cases.

Index Terms—Off-lattice AB model, protein folding, vortex search algorithm.

The authors are with the Istanbul Technical University, Istanbul, SARIYER 34469, Turkey (e-mail: bdogan@ itu.edu.tr, olmezt@itu.edu.tr).

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Cite: Berat Doğan and Tamer Ölmez, "Modified Off-lattice AB Model for Protein Folding Problem Using the Vortex Search Algorithm," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 329-333, 2015.

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