Parallel Evolutionary Computation in R

Title
Parallel Evolutionary Computation in R
Publication Date
2012
Author(s)
Gondro, Cedric
( author )
OrcID: https://orcid.org/0000-0003-0666-656X
Email: cgondro2@une.edu.au
UNE Id une-id:cgondro2
Kwan, Paul H
Editor
Editor(s): Shawkat Ali, Noureddine Abbadeni, Mohamed Batouche
Abstract
Chapter reprinted in Information Resources Management Association. (2013). Bioinformatics: Concepts, Methodologies, Tools, and Applications. Volume 1. Medical Information Science Reference, p. 105-129
Type of document
Book Chapter
Language
en
Entity Type
Publication
Publisher
Information Science Reference
Place of publication
Hershey, United States of America
Edition
1
DOI
10.4018/978-1-4666-1830-5.ch020
UNE publication id
une:12599
Abstract
Evolutionary Computation (EC) is a branch of Artificial Intelligence which encompasses heuristic optimization methods loosely based on biological evolutionary processes. These methods are efficient in finding optimal or near-optimal solutions in large, complex non-linear search spaces. While evolutionary algorithms (EAs) are comparatively slow in comparison to deterministic or sampling approaches, they are also inherently parallelizable. As technology shifts towards multi core and cloud computing, this overhead becomes less relevant, provided a parallel framework is used. In this chapter the authors discuss how to implement and run parallel evolutionary algorithms in the popular statistical programming language R. R has become the de facto language for statistical programming and it is widely used in bio statistics and bio informatics due to the availability of thousands of packages to manipulate and analyze data. It is also extremely easy to parallelize routines within R, which makes it a perfect environment for evolutionary algorithms. EC' is a large field of research, and many different algorithms have been proposed. While there is no single silver bullet that can handle all classes of problems, an algorithm that is extremely simple, efficient, and with good generalization properties is Differential Evolution (DE). Herein the authors discuss step-by-step how to implement DE in R and how to parallelize it. They then illustrate with a to y genome-wide association study (GWAS) how to indent candidate regions associated with a quantitative trait of interest.
Link
Citation
Multidisciplinary Computational Intelligence Techniques: Applications in Business, Engineering, and Medicine, p. 351-377
ISBN
9781466618305
9781466618312
9781466618329
Start page
351
End page
377

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