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Memetic Computing
Erschienen in: Memetic Computing 1/2016

01.03.2016 | Regular Research Paper

Differential evolution framework for big data optimization

verfasst von: Saber Elsayed, Ruhul Sarker

Erschienen in: Memetic Computing | Ausgabe 1/2016

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Abstract

During the last two decades, dealing with big data problems has become a major issue for many industries. Although, in recent years, differential evolution has been successful in solving many complex optimization problems, there has been research gaps on using it to solve big data problems. As a real-time big data problem may not be known in advance, determining the appropriate differential evolution operators and parameters to use is a combinatorial optimization problem. Therefore, in this paper, a general differential evolution framework is proposed, in which the most suitable differential evolution algorithm for a problem on hand is adaptively configured. A local search is also employed to increase the exploitation capability of the proposed algorithm. The algorithm is tested on the 2015 big data optimization competition problems (six single objective problems and six multi-objective problems). The results show the superiority of the proposed algorithm to several state-of-the-art algorithms.
Vorheriger Artikel Genetic programming for feature construction and selection in classification on high-dimensional data
Nächster Artikel Convex hull-based multi-objective evolutionary computation for maximizing receiver operating characteristics performance

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Fußnoten
1
The code is available upon request.
 
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Metadaten
Titel
Differential evolution framework for big data optimization
verfasst von
Saber Elsayed
Ruhul Sarker
Publikationsdatum
01.03.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 1/2016
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-015-0174-x

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