Multiobjective Teaching-Learning-Based Optimization (MO-TLBO) for motif finding
David L. González‐Álvarez, Miguel Angel Vega-Rodríguez, Juan Antonio Gomez-Pulido, Juan Manuel Sanchez-Perez · 2012
The Multiobjective Teaching-Learning-Based Optimization (MO-TLBO) is a new multiobjective evolutionary algorithm proposed for solving one of the most important optimization problems in Bioinformatics, the Motif Discovery Problem (MDP). The proposed algorithm is a multiobjective adaptation of the TLBO algorithm, a population-based optimizer that defines a set of individuals with the aim of increasing their knowledges (objective function values) by means of different learning phases. To demonstrate the effectiveness of our approximation we have solved a set of twelve biological instances belonging to different organisms. The obtained results show that the proposed method discovers better solutions than those obtained by several multiobjective evolutionary algorithms, and it achieves better predictions than those made by fourteen well-known biological methods.