Analysis of MOEA/D Approaches for Inferring Ancestral Relationships

• Sergio Santander-Jiménez ^[1] ; Vega-Rodríguez, Miguel A. ^[2] ; Leonel Sousa ^[1]
  1. [1] Universidade de Lisboa

     Universidade de Lisboa

     Socorro, Portugal

     
  2. [2] Universidad de Extremadura

     Universidad de Extremadura

     Badajoz, España

     
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 168-180
• Idioma: inglés
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• Resumen

  • 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.