Paper Info
Title
Evaluating the Performance of a Parallel Multiobjective Artificial Bee Colony Algorithm for Inferring Phylogenies on Multicore Architectures
Abstract
A wide variety of optimization problems requires the combination of Bioinspired and Parallel Computing to address the complexity needed to get optimal solutions in reduced times. The multicore era allows the researcher to exploit modern arqitectures to resolve these NP-Hard problems. Inferring phylogenetic trees which describe a hypothesis of the evolution of species is a well-known example of this kind of problems. As the space of possible tree topologies increases exponentially with the number of species, exhaustive searches cannot be applied. Also, additional difficulties arise when we must consider simultaneously multiple optimality measures to resolve the problem. In this paper, we report a performance study on multicore machines of a parallel multiobjective adaptation of the Artificial Bee Colony algorithm for inferring phylogenies according to the maximum parsimony and maximum likelihood criteria. Experimental results reveal that our proposal can improve other approaches based on advanced High Performance Computing techniques on large data sets.
Year
DOI
Venue
2012
10.1109/ISPA.2012.105
ISPA
Keywords
Field
DocType
inferring phylogenies,optimisation,multicore architectures,additional difficulty,multiobjective optimization,trees (mathematics),maximum likelihood criteria,inference mechanisms,high performance computing techniques,maximum likelihood criterion,np-hard problem,exhaustive search,colony algorithm,parallel multiobjective artificial bee colony algorithm,phylogenetic trees inference,artificial bee colony,multicore era,phylogenetic inference,multiprocessing systems,computational complexity,parallel algorithms,tree topologies,optimization problems,np-hard problems,multicore machines,performance evaluation,parallel computing,computer architecture,artificial bee colony algorithm,bioinformatics,maximum parsimony,parallel multiobjective artificial bee,multicore machine,advanced high performance computing,phylogeny,optimization,parallel processing,multicore processing,algorithm design and analysis,topology,np hard problems,vegetation
Artificial bee colony algorithm,Supercomputer,Parallel algorithm,Computer science,Theoretical computer science,Network topology,Multi-objective optimization,Artificial intelligence,Multi-core processor,Optimization problem,Machine learning,Computational complexity theory
Conference
ISBN
Citations 
PageRank 
978-1-4673-1631-6
2
0.38
References 
Authors
18
4
Name
Order
Citations
PageRank
Sergio Santander-Jiménez15815.11
Miguel A. Vega-Rodriguez214518.81
Juan Antonio Gómez Pulido3465.88
Juan Manuel Sánchez-Pérez427830.84