Title
Fine-grained parallelization of fitness functions in bioinformatics optimization problems: gene selection for cancer classification and biclustering of gene expression data.
Abstract
The results show better performances using reconfigurable hardware technology instead of usual microprocessors, in computing time and power consumption terms, not only because of the parallelization of the arithmetic operations, but also thanks to the concurrent fitness evaluation for several individuals of the population in the metaheuristic. This is a good basis for building accelerated and low-energy solutions for intensive computing scenarios.
Year
DOI
Venue
2016
10.1186/s12859-016-1200-9
BMC Bioinformatics
Keywords
Field
DocType
Biclustering,Cancer classification,FPGA,Fitness function,Floating-point arithmetic,Metaheuristics,Parallelism
Population,Computer science,Floating point,Parallel computing,Theoretical computer science,Fitness function,Fitness approximation,Biclustering,Bioinformatics,Optimization problem,Metaheuristic,Reconfigurable computing
Journal
Volume
Issue
ISSN
17
1
1471-2105
Citations 
PageRank 
References 
0
0.34
21
Authors
7