The motif discovery problem (MDP) is an important biological optimization problem that has been addressed in numerous ways. However, it is important to note that when we address real complex optimization problems, we should adequately formulate them in order to provide real applicability to the developed techniques. In the particular case of MDP, as we do not know the size of the motifs and the number of repetitions that can be found in the sequences, we must not make any length or pattern-repetition assumptions. In addition, if we consider that it is practically impossible to adequately formulate an optimization problem with a single-objective function formulation, multiobjective optimization can be a good methodology to be considered. In this paper, we propose a novel hybrid multiobjective algorithm for tackling the MDP. Our main objective is to study the results achieved by our algorithm, analysing its performance when different motif occurrence models are considered. As we will see, experimental results on different sets of real instances will point out the advantages and disadvantages of each model, also checking how a more realistic definition of the optimized problem provides better quality biological results.
- computational science
- motif discovery problem
- multiobjective optimization
- Shuffle Frog Leaping Algorithm
- swarm intelligence