Abstract
Throughout the years, decomposition approaches have been gaining major research attraction as a promising way to solve complex multiobjective optimization problems. This work investigates the application of decomposition-based optimization techniques to address a challenging problem from the bioinformatics domain: the reconstruction of ancestral relationships from protein data. A comparative analysis of different design alternatives for the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) is undertaken. Particularly, MOEA/D variants integrating genetic operators (MOEA/D-GA) and differential evolution (MOEA/D-DE) are studied. Hybrid search mechanisms are included to improve the accuracy of these methods, combining evolutionary strategies with problem-specific heuristics. Experimental results on four real-world problem instances give account of the significance of these techniques, especially when differential evolution approaches are used to conduct the search. As a result, significant multiobjective performance and biological solution quality are accomplished when compared with other methods from the literature.
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Acknowledgments
This work was partially funded by the AEI (State Research Agency, Spain) and the ERDF (European Regional Development Fund, EU), under the contract TIN2016-76259-P (PROTEIN project), as well as Portuguese national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) projects UID/CEC/50021/2019 and PTDC/CCI-COM/31901/2017 (HiPErBio). Sergio Santander-Jiménez is supported by the Post-Doctoral Fellowship from FCT under Grant SFRH/BPD/119220/2016.
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Santander-Jiménez, S., Vega-Rodríguez, M.A., Sousa, L. (2019). Analysis of MOEA/D Approaches for Inferring Ancestral Relationships. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_15
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