Evolutionary optimization: a big data perspective

Bhattacharya, Maumita, Islam, Rafiqul and Abawajy, Jemal 2016, Evolutionary optimization: a big data perspective, Journal of network and computer applications, vol. 59, pp. 416-426, doi: 10.1016/j.jnca.2014.07.032.

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Title Evolutionary optimization: a big data perspective
Author(s) Bhattacharya, Maumita
Islam, Rafiqul
Abawajy, JemalORCID iD for Abawajy, Jemal orcid.org/0000-0001-8962-1222
Journal name Journal of network and computer applications
Volume number 59
Start page 416
End page 426
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-01
ISSN 1084-8045
Keyword(s) Optimization
Big data
Evolutionary algorithm
High dimensionality
Non-linearity
Summary Stochastic search techniques such as evolutionary algorithms (EA) are known to be better explorer of search space as compared to conventional techniques including deterministic methods. However, in the era of big data like most other search methods and learning algorithms, suitability of evolutionary algorithms is naturally questioned. Big data pose new computational challenges including very high dimensionality and sparseness of data. Evolutionary algorithms' superior exploration skills should make them promising candidates for handling optimization problems involving big data. High dimensional problems introduce added complexity to the search space. However, EAs need to be enhanced to ensure that majority of the potential winner solutions gets the chance to survive and mature. In this paper we present an evolutionary algorithm with enhanced ability to deal with the problems of high dimensionality and sparseness of data. In addition to an informed exploration of the solution space, this technique balances exploration and exploitation using a hierarchical multi-population approach. The proposed model uses informed genetic operators to introduce diversity by expanding the scope of search process at the expense of redundant less promising members of the population. Next phase of the algorithm attempts to deal with the problem of high dimensionality by ensuring broader and more exhaustive search and preventing premature death of potential solutions. To achieve this, in addition to the above exploration controlling mechanism, a multi-tier hierarchical architecture is employed, where, in separate layers, the less fit isolated individuals evolve in dynamic sub-populations that coexist alongside the original or main population. Evaluation of the proposed technique on well known benchmark problems ascertains its superior performance. The algorithm has also been successfully applied to a real world problem of financial portfolio management. Although the proposed method cannot be considered big data-ready, it is certainly a move in the right direction.
Language eng
DOI 10.1016/j.jnca.2014.07.032
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 890101 Fixed Line Data Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070779

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