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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

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


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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: 741 | Views: 3,155

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

Abstract
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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

[1] E. Alba and J. M. Troya, "A survey of parallel distributed genetic algorithms", Journal Complexity, Vol. 4, No. 4, pp. 31-52, 1999.

[2] E. Cantu-Paz, "A summary of research on parallel genetic algorithms," in IlliGAL report 95007, University of Illinois at Urbana-Champaign, 1995.

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[CrossRef] [SCOPUS Times Cited 10]


[4] T. Murata, K. Takada, "Performance evaluation of a distributed genetic algorithm with cellular structures on function optimization problems," in Proceedings of 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems. Springer-Verlag, 2004, pp. 1128 - 1135.
[CrossRef] [SCOPUS Times Cited 3]


[5] S. Baluja, "Structure and performance of fine-grain parallelism in genetic search," technical report, Carnegie Mellon University, 1993.

[6] S. Gordon, K. Mathias, and D. Whitley, "Cellular genetic algorithms as function optimizers: Locality effects," in In Proceedings of the ACM Symposium on Applied Computing, pp. 237 - 241, 1994.
[CrossRef] [SCOPUS Times Cited 8]


[7] J. Sarma and K. D. Jong, "An analysis of the effects of neighborhood size and shape on local selection algorithms," in Parallel Problem Solving from Nature, pp. 236-244, Springer, 1996.

[8] E. Alba and B. Dorronsoro, "Cellular genetic algorithms," in Operations Research / Computer Science Interfaces, Springer, 2008.
[CrossRef] [SCOPUS Times Cited 76]


[9] E. Alba and B. Dorronsoro, "The exploration/exploitation tradeoff in dynamic cellular genetic algorithms," in IEEE Transactions on Evolutionary Computation, IEEE, 2005.
[CrossRef] [Web of Science Times Cited 291] [SCOPUS Times Cited 376]


[10] M. Kirley, X. Li, and D. G. Green, "Investigation of a cellular genetic algorithm that mimics landscape ecology," in Proceedings of SEAL'98, Lecture Notes in Computer Science, pp. 90-97, Springer-Verlag Berlin Heidelberg, 1999.
[CrossRef] [SCOPUS Times Cited 18]


[11] E. Alba and J. Troya, "Improving flexibility and efficiency by adding parallelism to genetic algorithms," in Statistics and Computing, pp. 12(2):91-114, Kluwer Academic Publishers, 2002.
[CrossRef] [Web of Science Times Cited 63] [SCOPUS Times Cited 80]


[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 7] [SCOPUS Times Cited 14]


[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 18] [SCOPUS Times Cited 36]


[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 72] [SCOPUS Times Cited 85]


[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.
[CrossRef] [SCOPUS Times Cited 6]


[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 7] [SCOPUS Times Cited 11]


[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.
[CrossRef] [SCOPUS Times Cited 18]


[20] M. Tomassini, "Spatially Structured Evolutionary Algorithms", Artificial Evolution in Space and Time. Springer. Series: Natural Computing Series, 2005.

[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.
[CrossRef]


[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.
[CrossRef] [Web of Science Times Cited 64]


[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.
[CrossRef]


[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: 522 TCR
SCOPUS® Citations for all references: 743 TCR

Web of Science® Average Citations per reference: 20 ACR
SCOPUS® Average Citations per reference: 29 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 2024-06-04 08:03 in 115 seconds.




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Stefan cel Mare University of Suceava, Romania


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