Please use this identifier to cite or link to this item:
http://hdl.handle.net/10662/19103
Title: | A multiobjective adaptive approach for the inference of evolutionary relationships in protein-based scenarios |
Authors: | Santander Jiménez, Sergio Vega Rodríguez, Miguel Ángel Sousa, Leonel |
Keywords: | Computación bioinspirada;Bioinspired computing;Optimización multiobjetivo;Multiobjective optimization;Algoritmos adaptativos;Adaptive algorithms;Bioinformática;Bioinformatics |
Issue Date: | 2019 |
Publisher: | Elsevier |
Abstract: | Complex optimization problem solving is a constant issue in a wide range of scientific domains. Robust bioinspired procedures with accurate search capabilities are therefore required to address the challenge that such optimization problems represent. This work explores different design alternatives for the metaheuristic Multiobjective Shuffled Frog-Leaping Algorithm, a novel method that combines parallel searches and swarm-based operators to undertake the processing of complex search spaces. Three variants of the metaheuristic are adopted: a dominance-based approach, an indicator-based alternative, and an adaptive proposal that incorporates both multiobjective strategies (dynamically assigning during the execution more resources to the most successful strategy). The performance of the proposed designs is examined when tackling, as a case study, the inference of ancestral relationships from protein data, using different multiobjective metrics and bio-statistical testing procedures. Experimental results show the additional robustness that the adaptive technique provides to the metaheuristic, allowing its search engine to exploit the most fitting multiobjective approach according to the status of the optimization process. |
Description: | Publicado en: Information Sciences (Volume: 485, June 2019, pp. 281-300). http://dx.doi.org/10.1016/j.ins.2019.02.020 |
URI: | http://hdl.handle.net/10662/19103 |
ISSN: | 0020-0255 |
DOI: | 10.1016/j.ins.2019.02.020 |
Appears in Collections: | DTCYC - Artículos |
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File | Description | Size | Format | |
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j_ins_2019_02_020.pdf | 2,71 MB | Adobe PDF | View/Open |
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