Title
A multiobjective adaptive approach for the inference of evolutionary relationships in protein-based scenarios.
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.
Year
DOI
Venue
2019
10.1016/j.ins.2019.02.020
Information Sciences
Keywords
Field
DocType
Bioinspired computing,Multiobjective optimization,Adaptive algorithms,Bioinformatics
Search engine,Inference,Exploit,Robustness (computer science),Operator (computer programming),Artificial intelligence,Optimization problem,Machine learning,Mathematics,Metaheuristic
Journal
Volume
ISSN
Citations 
485
0020-0255
1
PageRank 
References 
Authors
0.39
0
3
Name
Order
Citations
PageRank
Sergio Santander-Jiménez15815.11
Miguel A. Vega-Rodríguez2741113.05
Leonel Sousa31210145.50