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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: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-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 in 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: 338 | Views: 1,387

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

[1] E. Alba and J. M. Troya, "Improving flexibility and efficiency by adding parallelism to genetic algorithms," Statistics and Computing, vol. 12, no. 2, pp. 91-114, 2002.
[CrossRef] [Web of Science Times Cited 51]


[2] N. Xiao and M. P. Armstrong, "A specialized island model and its application in multiobjective optimization," in Proc. of Genetic and Evolutionary Computation Conference, pp. 1530-1540, 2003.

[3] G. A. Sena, D. Megherbi, and G. Isern, "Implementation of a parallel genetic algorithm on a cluster of workstations: traveling salesman problem, a case study," Future Generation Computer Systems, vol. 17, no. 4, pp. 477-488, 2001.
[CrossRef] [Web of Science Times Cited 32]


[4] L. Wang, A. Maciejewski, H. Siegel, V. Roychowdhury, and B. Eldridge, "A study of five parallel approaches to a genetic algorithm for the traveling salesman problem," Intelligent Automation & Soft Computing, vol. 11, no. 4, pp. 217-234, 2005.
[CrossRef] [Web of Science Times Cited 10]


[5] Y. Fan, T. Jiang, and D. J. Evans, "Volumetric segmentation of brain images using parallel genetic algorithms," IEEE Transactions on Medical Imaging, vol. 21, no. 8, pp. 904-909, 2002.
[CrossRef] [Web of Science Times Cited 53]


[6] T. Jiang and Y. Fan, "Parallel genetic algorithm for 3D medical image analysis," in Proc. of IEEE International Conference on Systems, Man and Cybernetics, vol. 6, 2003.

[7] J. I. Hidalgo, M. Prieto, J. Lanchares, F. Tirado, B. De Andres, S. Esteban, and D. Rivera, "A method for model parameter identification using parallel genetic algorithms," Recent Advances in Parallel Virtual Machine and Message Passing Interface, vol. 1697, pp. 291-298, 1999.
[CrossRef]


[8] T. Hiroyasu, M. Miki, and M. Negami, "Distributed genetic algorithms with randomized migration rate," Systems, Man and Cybernetics, vol. 1, pp. 689-694, 1999.

[9] M. Rebaudengo and M. S. Reorda, "An experimental analysis of the effects of migration in parallel genetic algorithms," in Proc. of Euromicro Workshop on Parallel and Distributed Processing, pp. 232-238, 1993.

[10] E. Alba and J. M. Troya, "A survey of parallel distributed genetic algorithms," Complexity, vol. 4, no. 4, pp. 31-52, 1999.
[CrossRef]


[11] E. Cantú-Paz, "Migration policies, selection pressure, and parallel evolutionary algorithms," Journal of heuristics, vol. 7, no. 4, pp. 311-334, 2001.
[CrossRef] [Web of Science Times Cited 95]


[12] E. Cantú-Paz, "Topologies, migration rates, and multi-population parallel genetic algorithms," in Proc. of the Genetic and Evolutionary Computation Conference, San Francisco, pp. 91-98, 1999.

[13] E. Cantú-Paz, "Markov chain models of parallel genetic algorithms," IEEE Transactions on Evolutionary Computation, vol. 4, no. 3, pp. 216-226, 2000.
[CrossRef] [Web of Science Times Cited 21]


[14] E. Cantu-Paz, "On the effects of migration on the fitness distribution of parallel evolutionary algorithms," Lawrence Livermore National Lab., CA (US), UCRL-JC-138729, 2000.

[15] J. Berntsson and M. Tang, "A convergence model for asynchronous parallel genetic algorithms," in Proc. of The 2003 Congress on Evolutionary Computation, vol. 4, pp. 2627-2634, 2003.
[CrossRef]


[16] Y. Maeda, M. Ishita, and Q. Li, "Fuzzy adaptive search method for parallel genetic algorithm with island combination process," International Journal of Approximate Reasoning, vol. 41, no. 1, pp. 59-73, 2006.
[CrossRef] [Web of Science Times Cited 11]


[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 40]


[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 81]


[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.

[20] T. Friedrich, P. S. Oliveto, D. Sudholt, and C. Witt, "Analysis of diversity-preserving mechanisms for global exploration," Evolutionary Computation, vol. 17, no. 4, pp. 455-476, 2009.
[CrossRef] [Web of Science Times Cited 38]


[21] D. E. Goldberg, The design of innovation: Lessons from and for competent genetic algorithms. Springer Science & Business Media, Dallas, TX, U.S.A., pp. 132-141, 2013.

[22] J. Gu, X. Gu, and M. Gu, "A novel parallel quantum genetic algorithm for stochastic job shop scheduling," Journal of Mathematical Analysis and Applications, vol. 355, no. 1, pp. 63-81, 2009.
[CrossRef] [Web of Science Times Cited 68]


[23] J. Denzinger and J. Kidney, "Improving migration by diversity," in Proc. of The Congress on Evolutionary Computation, vol. 1, pp. 700-707, 2003.
[CrossRef] [Web of Science Times Cited 8]


[24] L. Singh and S. Kumar, "Migration based parallel differential evolution learning in Asymmetric Subsethood Product Fuzzy Neural Inference System: A simulation study," in proc. of IEEE Congress on Evolutionary Computation, pp. 1608-1613, 2007.
[CrossRef] [Web of Science Times Cited 1]


[25] M. Lozano, F. Herrera, and J. R. Cano, "Replacement strategies to preserve useful diversity in steady-state genetic algorithms," Information Sciences, vol. 178, no. 23, pp. 4421-4433, 2008.
[CrossRef] [Web of Science Times Cited 83]


[26] Q. Li and Y. Maeda, "Distributed adaptive search method for genetic algorithm controlled by fuzzy reasoning," in Proc. of IEEE International Conference on Fuzzy Systems, pp. 2022-2027, 2008.
[CrossRef]


[27] R. W. Morrison and K. A. De Jong, "Measurement of population diversity," Artificial Evolution, pp. 31-41, 2001.



References Weight

Web of Science® Citations for all references: 592 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 21 ACR
SCOPUS® Average Citations per reference: 0

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 2019-08-20 00:04 in 123 seconds.




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


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