Solving optimization problems in Nimrod/OK using a Genetic Algorithm

Lim, Yu Hua, Tan, Jefferson and Abramson, David (2012). Solving optimization problems in Nimrod/OK using a Genetic Algorithm. In: Proceedings of the International Conference On Computational Science, Iccs 2012. International Conference on Computational Science (ICCS), Omaha Ne, (1647-1656). 04-06 June 2012. doi:10.1016/j.procs.2012.04.182


Author Lim, Yu Hua
Tan, Jefferson
Abramson, David
Title of paper Solving optimization problems in Nimrod/OK using a Genetic Algorithm
Conference name International Conference on Computational Science (ICCS)
Conference location Omaha Ne
Conference dates 04-06 June 2012
Proceedings title Proceedings of the International Conference On Computational Science, Iccs 2012   Check publisher's open access policy
Journal name Procedia Computer Science   Check publisher's open access policy
Place of Publication Amsterdam, The Netherlands
Publisher Elsevier BV
Publication Year 2012
Year available 2012
Sub-type Fully published paper
DOI 10.1016/j.procs.2012.04.182
Open Access Status Not Open Access
ISSN 1877-0509
Volume 9
Start page 1647
End page 1656
Total pages 10
Language eng
Formatted Abstract/Summary
A scientific workflow can be viewed as formal model of the flow of data between processing components. It often involves a combination of data integration, computation, analysis, and visualization steps. An emerging use case involves determining some input parameters that minimize (or maximize) the output of a computation. Kepler is a good framework for specifying such optimizations because arbitrary computations can be composed into a pipeline, which is then repeated until an optimal set of inputs is found. Genetic Algorithms are generic optimization algorithms based on the principles of genetics and natural selection, and are well suited for models with discontinuous objective functions. This paper discusses an implementation of a Genetic Algorithm in Kepler, building on the Nimrod/OK framework. The resulting tool is generic and flexible enough to support a variety of experimental domains. The paper reports a number of experiments that demonstrate the performance with a set of benchmarking functions.
Keyword Scientific Worklows
Kepler
Genetic Algorithms Optimization
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

 
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Created: Tue, 22 Oct 2013, 01:57:42 EST by Ms Diana Cassidy on behalf of Research Computing Centre