Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/12392
Title: Parallel Evolutionary Computation in R
Contributor(s): Gondro, Cedric  (author)orcid ; Kwan, Paul H  (author)
Publication Date: 2012
DOI: 10.4018/978-1-4666-1830-5.ch020
Handle Link: https://hdl.handle.net/1959.11/12392
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.
Publication Type: Book Chapter
Source of Publication: Multidisciplinary Computational Intelligence Techniques: Applications in Business, Engineering, and Medicine, p. 351-377
Publisher: Information Science Reference
Place of Publication: Hershey, United States of America
ISBN: 9781466618305
9781466618312
9781466618329
Field of Research (FOR): 080108 Neural, Evolutionary and Fuzzy Computation
080301 Bio informatics Software
080501 Distributed and Grid Systems
HERDC Category Description: B1 Chapter in a Scholarly Book
Other Links: http://trove.nla.gov.au/version/178336445
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