Heterogeneous cooperative co-evolution memetic differential evolution algorithms for big data optimisation problems

Sabar, Nasser R., Abawajy, Jemal and Yearwood, John Leighton 2017, Heterogeneous cooperative co-evolution memetic differential evolution algorithms for big data optimisation problems, IEEE transactions on evolutionary computation, vol. 21, no. 2, pp. 315-327, doi: 10.1109/TEVC.2016.2602860.

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Title Heterogeneous cooperative co-evolution memetic differential evolution algorithms for big data optimisation problems
Author(s) Sabar, Nasser R.
Abawajy, JemalORCID iD for Abawajy, Jemal orcid.org/0000-0001-8962-1222
Yearwood, John LeightonORCID iD for Yearwood, John Leighton orcid.org/0000-0002-7562-6767
Journal name IEEE transactions on evolutionary computation
Volume number 21
Issue number 2
Start page 315
End page 327
Total pages 13
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2017-04
ISSN 1089-778X
Keyword(s) Differential evolution
Memeitc algorithm
Adaptive algorithm
Big data
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
differential evolution (DE)
memetic algorithm
Summary Evolutionary algorithms (EAs) have recently been suggested as candidate for solving big data optimisation problems that involve very large number of variables and need to be analysed in a short period of time. However, EAs face scalability issue when dealing with big data problems. Moreover, the performance of EAs critically hinges on the utilised parameter values and operator types, thus it is impossible to design a single EA that can outperform all other on every problem instances. To address these challenges, we propose a heterogeneous framework that integrates a cooperative co-evolution method with various types of memetic algorithms. We use the cooperative co-evolution method to split the big problem into sub-problems in order to increase the efficiency of the solving process. The subproblems are then solved using various heterogeneous memetic algorithms. The proposed heterogeneous framework adaptively assigns, for each solution, different operators, parameter values and local search algorithm to efficiently explore and exploit the search space of the given problem instance. The performance of the proposed algorithm is assessed using the Big Data 2015 competition benchmark problems that contain data with and without noise. Experimental results demonstrate that the proposed algorithm, with the cooperative co-evolution method, performs better than without cooperative co-evolution method. Furthermore, it obtained very competitive results for all tested instances, if not better, when compared to other algorithms using a lower computational times.
Language eng
DOI 10.1109/TEVC.2016.2602860
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30086985

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