Abstract
The hydrophobic-polar (HP) model for protein structure prediction abstracts the fact that hydrophobic interactions are a dominant force in the protein folding process. This model represents a hard combinatorial optimization problem, which has been widely addressed using evolutionary algorithms and other metaheuristics. In this paper, the multiobjectivization of the HP model is proposed. This originally single-objective problem is restated as a multiobjective one by decomposing the conventional objective function into two independent objectives. By using different evolutionary algorithms and a large set of test cases, the new alternative formulation was compared against the conventional single-objective problem formulation. As a result, the proposed formulation increased the search performance of the implemented algorithms in most of the cases. Both two- and three-dimensional lattices are considered. To the best of authors’ knowledge, this is the first study where multiobjective optimization methods are used for solving the HP model.
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Garza-Fabre, M., Rodriguez-Tello, E., Toscano-Pulido, G. (2012). Multiobjectivizing the HP Model for Protein Structure Prediction. In: Hao, JK., Middendorf, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2012. Lecture Notes in Computer Science, vol 7245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_16
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