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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 644266260
doi: 10.4316/AECE


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2016-Jun-14
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  3/2016 - 11

Parallel Genetic Algorithms with Dynamic Topology using Cluster Computing

ADAR, N. See more information about ADAR, N. on SCOPUS See more information about ADAR, N. on IEEExplore See more information about ADAR, N. on Web of Science, KUVAT, G. See more information about KUVAT, G. on SCOPUS See more information about KUVAT, G. on SCOPUS See more information about KUVAT, G. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,489 KB) | Citation | Downloads: 147 | Views: 201

Author keywords
genetic algorithms, network topology, message passing, parallel architectures, parallel programming

References keywords
genetic(22), parallel(21), algorithms(16), evolutionary(9), migration(8), computation(7), algorithm(6), systems(5), fuzzy(5), distributed(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 73 - 80
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03011
Web of Science Accession Number: 000384750000011
SCOPUS ID: 84991108524

Abstract
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A parallel genetic algorithm (PGA) conducts a distributed meta-heuristic search by employing genetic algorithms on more than one subpopulation simultaneously. PGAs migrate a number of individuals between subpopulations over generations. The layout that facilitates the interactions of the subpopulations is called the topology. Static migration topologies have been widely incorporated into PGAs. In this article, a PGA with a dynamic migration topology (D-PGA) is proposed. D-PGA generates a new migration topology in every epoch based on the average fitness values of the subpopulations. The D-PGA has been tested against ring and fully connected migration topologies in a Beowulf Cluster. The D-PGA has outperformed the ring migration topology with comparable communication cost and has provided competitive or better results than a fully connected migration topology with significantly lower communication cost. PGA convergence behaviors have been analyzed in terms of the diversities within and between subpopulations. Conventional diversity can be considered as the diversity within a subpopulation. A new concept of permeability has been introduced to measure the diversity between subpopulations. It is shown that the success of the proposed D-PGA can be attributed to maintaining a high level of permeability while preserving diversity within subpopulations.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef]


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[CrossRef] [Web of Science Times Cited 71] [SCOPUS Times Cited 98]


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[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 34]


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[CrossRef] [SCOPUS Times Cited 9]


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[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 14]


[17] E. Alba, F. Luna, A. J. Nebro, and J. M. Troya, "Parallel heterogeneous genetic algorithms for continuous optimization," Parallel Computing, vol. 30, no. 5, pp. 699-719, 2004.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 43]


[18] E. Alba and J. M. Troya, "Analyzing synchronous and asynchronous parallel distributed genetic algorithms," Future Generation Computer Systems, vol. 17, no. 4, pp. 451-465, 2001.
[CrossRef] [Web of Science Times Cited 63] [SCOPUS Times Cited 100]


[19] S.-K. Oh, C. T. Kim, and J.-J. Lee, "Balancing the selection pressures and migration schemes in parallel genetic algorithms for planning multiple paths," in Proc. of IEEE International Conference on Robotics and Automation, vol. 4, pp. 3314-3319, 2001.

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References Weight

Web of Science® Citations for all references: 427 TCR
SCOPUS® Citations for all references: 639 TCR

Web of Science® Average Citations per reference: 15 ACR
SCOPUS® Average Citations per reference: 23 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2016-12-08 23:06 in 97 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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