Using a Parallel Team of Multiobjective Evolutionary Algorithms to Solve the Motif Discovery Problem

This paper proposes the use of a parallelmultiobjective evolutionary technique to predict patterns, motifs, in real deoxyribonucleic acid (DNA) sequences. DNA analysis is a very important branch within bioinformatics, resulting in a large number of NP-hard optimization problems such as multiple alignment, motif finding, or protein folding. In this work we study the use of amultiobjective evolutionary algorithms team to solve the Motif Discovery Problem. According to this, we have designed a parallel heuristic that allows the collaborative work of four algorithms, two population-based algorithms: Differential Evolution with Pareto Tournaments and Nondominated Sorting Genetic Algorithm II, and two trajectory-based algorithms: Multiobjective Variable Neighborhood Search and Multiobjective Skewed Variable Neighborhood Search. In this way, we take advantage of the properties of different algorithms, getting to expand the search space covered in our problem. As we will see, the results obtained by our team significantly improve the results published in previous research.