Paper Info
Title
Phylogenetic Reconstructions Using An Indicator-Based Bat Algorithm For Multicore Processors
Abstract
The adoption of bioinspired methods to tackle optimization problems is progressively gaining major interest in different scientific domains. However, real-world problems often involve the exploration of very large search spaces and time-demanding operations that make mandatory the usage of parallel computing techniques. In this work, we address the reconstruction of ancestral relationships from DNA and protein data by parallelizing an indicator-based multiobjective adaptation of the Bat algorithm. The proposed parallel design is aimed at exploiting the capabilities of multicore CPUs in commodity-like systems, which are among the most common hardware setups currently employed for biological research. Experiments in four real-world datasets point out the practical interest of the proposal in terms of parallel performance and solution quality, reporting relevant results in comparison to state-of-the-art methods for the tackled biological problem.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621188
PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
Field
DocType
Bioinspired Computing, High Performance Computing, Multiobjective Optimization, Multicore Processors, Bioinformatics
Phylogenetic tree,Bat algorithm,Supercomputer,Computer science,Multi-objective optimization,Artificial intelligence,Multi-core processor,Biological Problem,Computer engineering,Optimization problem,Machine learning
Conference
ISSN
Citations 
PageRank 
2156-1125
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Sergio Santander-Jiménez15815.11
Miguel A. Vega-Rodríguez2741113.05
Leonel Sousa31210145.50