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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Feb 2019
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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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.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  3/2012 - 6

A Structure-Based Coarse-Fine Approach for Diversity Tuning in Cellular GAs

MORALES-REYES, A. See more information about MORALES-REYES, A. on SCOPUS See more information about MORALES-REYES, A. on IEEExplore See more information about MORALES-REYES, A. on Web of Science, ERDOGAN, A. T. See more information about ERDOGAN, A. T. on SCOPUS See more information about ERDOGAN, A. T. on SCOPUS See more information about ERDOGAN, A. T. on Web of Science
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Download PDF pdficon (790 KB) | Citation | Downloads: 344 | Views: 2,129

Author keywords
evolutionary computation, genetic algorithms, parallel algorithms, optimization, adaptive algorithm

References keywords
genetic(16), cellular(14), algorithms(14), evolutionary(11), computation(7), parallel(6), optimization(6), alba(6), algorithm(5), systems(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 39 - 46
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03006
Web of Science Accession Number: 000308290500006
SCOPUS ID: 84865858905

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This article empirically assesses a coarse-fine approach for diversity tuning in cellular Genetic Algorithms (cGAs). The coarse tuning is performed through the constant reconfiguration of the grid while the fine tuning is locally achieved through dynamic anisotropic selection which considers individuals' locations in the local neighborhood. Benchmark problems including continuous, real-world and combinatorial problems are evaluated. The experimental results show an improvement in cGAs performance when compared to having a fixed topology configuration or to independently applying dynamic lattice reconfiguration or dynamic anisotropic.

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

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[CrossRef] [SCOPUS Times Cited 41]

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[CrossRef] [Web of Science Times Cited 147] [SCOPUS Times Cited 206]

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[CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 67]

[12] A. Morales-Reyes, E. Stefatos, A. Erdogan, and T. Arslan, "Towards fault-tolerant systems based on adaptive cellular genetic algorithms," in Proceedings of IEEE NASA/ESA Conference on Adaptive Hardware and Systems, pp. 398- 405, IEEE, 2008.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 12]

[13] D. A. Pierre, Optimization Theory with Applications. DOVER. 2nd. Edition, 1986.

[14] M. Kirley, "A cellular genetic algorithm with disturbances: Optimization using dynamic spatial interactions," Journal of Heuristics, Kluwer Academic Publishers, vol. 8, pp. 321-342, 2002.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 29]

[15] A. Morales-Reyes, A. Erdogan, and T. Arslan, "Lattice reconfiguration vs. local selection criteria for diversity tuning in cellular gas," in Proceedings of the 2010 IEEE Congress on Evolutionary Computation, pp. 1 - 8, IEEE, 2010.
[CrossRef] [SCOPUS Times Cited 2]

[16] M. Giacobini, M. Tomassini, A. Tettamanzi, and E. Alba, "Selection intensity in cellular evolutionary algorithms for regular lattices," in IEEE Transactions on Evolutionary Computation, pp. 489-505, 2005.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 67]

[17] D. Simoncini, P. Collard, S. Verel, and M. Clergue, "From cells to islands: An unified model of cellular parallel genetic algorithms," in Int. Conf. on Cellular Automata, pp. 248-257, Springer-Verlag, 2006.

[18] D. Simoncini, P. Collard, S. Verel, and M. Clergue, "On the influence of selection operators on performances in cellular genetic algorithms," in Proceedings of IEEE Congress on Evolutionary Computation (CEC'07). IEEE, 2007, pp. 4706-4713.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 9]

[19] T. Back and R. Breukelaar, "Using genetic algorithms to evolve behaviour in cellular automata," in Lecture Notes in Computer Sciences 3699, pp. 1-10, Springer-Verlag, 2005.

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[21] B. Dorronsoro and E. Alba, "A simple cellular genetic algorithm for continous optimization," in Proceedings of 2006 IEEE Congress on Evolutionary Computation, IEEE, 2006.

[22] H. Bersini, M. Dorigo, S. Langerman, G. Geront, and L. Gambardella, "Results of the first international contest on evolutionary optimization," in Proceedings of IEEE International Conference on Evolutionary Computation, pp. 611-615, IEEE, 1996.
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[23] E. Stefatos and T. Arslan, "High-performance adaptive GPS attitude determination VLSI architecture," in IEEE Workshop on Signal Processing Systems, SIPS. IEEE, 2004, pp. 233-238.

[24] J. Xu, T. Arslan, Q. Wang, and D. Wan, "An EHW architecture for real-time GPS attitude determination based on parallel genetic algorithm," in Proceedings of Conference on Evolvable Hardware NASA/DoD, 2002, pp. 133-141.

[25] D. Ortiz-Boyer, C. Hervs-Martnez, and N. Garca-Pedrajas. (2008) Benchmark problems [Online] Available: Temporary on-line reference link removed - see the PDF document

References Weight

Web of Science® Citations for all references: 338 TCR
SCOPUS® Citations for all references: 449 TCR

Web of Science® Average Citations per reference: 13 ACR
SCOPUS® Average Citations per reference: 17 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 2019-03-22 07:00 in 120 seconds.

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

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