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http://hdl.handle.net/1942/26182
Title: | Reproducible Roulette Wheel Sampling for Message Passing Environments | Authors: | NEMETH, Balazs HABER, Tom LIESENBORGS, Jori LAMOTTE, Wim |
Issue Date: | 2018 | Publisher: | Springer | Source: | Shi, Yong; Fu, Haohuan; Tian, Yingjie; Krzhizhanovskaya, Valeria V.; Lees, Michael Harold; Dongarra, Jack; Sloot, Peter M. A. (Ed.). Computational Science – ICCS 2018, Springer,p. 799-805 | Series/Report: | Lecture Notes in Computer Science | Series/Report no.: | 10861 | Abstract: | Roulette Wheel Sampling, sometimes referred to as Fitness Proportionate Selection, is a method to sample from a set of objects each with an associated weight. This paper introduces a distributed version of the method designed for message passing environments. Theoretical bounds are derived to show that the presented method has better scalability than naive approaches. This is verified empirically on a test cluster, where improved speedup is measured. In all tested configurations, the presented method performs better than naive approaches. Through a renumbering step, communication volume is minimized. This step also ensures reproducibility regardless of the underlying architecture. | Keywords: | genetic algorithms; roulette wheel selection; sequential; Monte Carlo; HPC; message passing | Document URI: | http://hdl.handle.net/1942/26182 | ISBN: | 9783319937007 | DOI: | 10.1007/978-3-319-93701-4_63 | Rights: | (C) Springer International Publishing AG, part of Springer Nature 2018 | Category: | C1 | Type: | Proceedings Paper | Validations: | vabb 2020 |
Appears in Collections: | Research publications |
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iccs2018_balazs_nemeth.pdf Restricted Access | Published version | 191.31 kB | Adobe PDF | View/Open Request a copy |
_469698_1_En_63_Chapter_Author.pdf | Peer-reviewed author version | 392.41 kB | Adobe PDF | View/Open |
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