A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
FMGA: Finding Motifs by Genetic Algorithm
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
MDGA: motif discovery using a genetic algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Bacterial Foraging Optimization Algorithm Integrating Tabu Search for Motif Discovery
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
Motif Discovery Using Evolutionary Algorithms
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery
Engineering Applications of Artificial Intelligence
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In this paper we present a novel local search for improving the ability of multiobjective evolutionary algorithms when finding repeated patterns -motifs- in DNA sequences. In the metaheuristic design, two competing goals must be taken into account: exploration and exploitation. Exploration is needed to cover most of the optimization problem search space and provide a reliable estimation of the global optimum. In turn, exploitation is also important since normally the solutions refinement allows the achievement of better results. In this work we take advantage of both concepts by combining the exploration capabilities of a population-based evolutionary algorithm and the power of a local search, especially designed to optimize the Motif Discovery Problem (MDP). For doing this, we have implemented a new hybrid multiobjective metaheuristic based on Artificial Bee Colony (ABC). After analyzing the results achieved by this algorithm, named Hybrid-MOABC (H-MOABC), and comparing them with those achieved by three multiobjective evolutionary algorithms and thirteen well-known biological tools, we prove that the hybridization computes accurate biological predictions on real genetic instances in an optimum way. In fact, to the best of our knowledge, the results presented in this paper improve those presented in the literature.