Locality-based Multiobjectivization for the HP Model of Protein Structure Prediction

Even under the rather simplified HP lattice model, protein structure prediction remains a challenging problem in combinatorial optimization. Recently, the multiobjectivization of this problem was proposed. By decomposing the original objective function, a two-objective formulation for the HP model was defined. Such an alternative formulation showed very promising results, leading to an increased search performance in most of the conducted experiments. This paper introduces a novel multiobjectivization for the HP model which is based on the locality notion of amino acid interactions. Using different evolutionary algorithms, this proposal was compared with respect to both the conventional single-objective formulation and the previously reported multiobjectivization. The new proposed formulation scored the best results in most of the cases. Statistical significance testing and a large set of test cases support the findings of this study. Results are provided for both the two-dimensional square lattice and the three-dimensional cubic lattice.