|3/2016 - 11|
Parallel Genetic Algorithms with Dynamic Topology using Cluster ComputingADAR, N. , KUVAT, G.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,489 KB) | Citation | Downloads: 178 | Views: 295|
genetic algorithms, network topology, message passing, parallel architectures, parallel programming
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
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|
| 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 46] [SCOPUS Times Cited 65]
 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.
 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 25] [SCOPUS Times Cited 36]
 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.
 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 43] [SCOPUS Times Cited 59]
 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.
 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] [SCOPUS Times Cited 3]
 T. Hiroyasu, M. Miki, and M. Negami, "Distributed genetic algorithms with randomized migration rate," Systems, Man and Cybernetics, vol. 1, pp. 689-694, 1999.
 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.
 E. Alba and J. M. Troya, "A survey of parallel distributed genetic algorithms," Complexity, vol. 4, no. 4, pp. 31-52, 1999.
 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 75] [SCOPUS Times Cited 101]
 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.
 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 20] [SCOPUS Times Cited 34]
 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.
 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] [SCOPUS Times Cited 9]
 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 10] [SCOPUS Times Cited 15]
 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 34] [SCOPUS Times Cited 43]
 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 65] [SCOPUS Times Cited 102]
 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.
 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 25] [SCOPUS Times Cited 33]
 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.
 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 38] [SCOPUS Times Cited 58]
 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 5] [SCOPUS Times Cited 12]
 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] [SCOPUS Times Cited 2]
 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 64] [SCOPUS Times Cited 77]
 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] [SCOPUS Times Cited 2]
 R. W. Morrison and K. A. De Jong, "Measurement of population diversity," Artificial Evolution, pp. 31-41, 2001.
Web of Science® Citations for all references: 451 TCR
SCOPUS® Citations for all references: 651 TCR
Web of Science® Average Citations per reference: 16 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 background updated on 2017-02-18 07:13 in 117 seconds.
Note1: Web of Science® is a registered trademark of Thomson Reuters.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.