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Efficacy of Different Strategies in Graph Coloring with Parallel Genetic Algorithms

F.E. Sangari1 , M. Nabahat2

Section:Research Paper, Product Type: Journal Paper
Volume-2 , Issue-5 , Page no. 138-141, May-2014

Online published on May 31, 2014

Copyright © F.E. Sangari, M. Nabahat . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: F.E. Sangari, M. Nabahat, “Efficacy of Different Strategies in Graph Coloring with Parallel Genetic Algorithms,” International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.138-141, 2014.

MLA Style Citation: F.E. Sangari, M. Nabahat "Efficacy of Different Strategies in Graph Coloring with Parallel Genetic Algorithms." International Journal of Computer Sciences and Engineering 2.5 (2014): 138-141.

APA Style Citation: F.E. Sangari, M. Nabahat, (2014). Efficacy of Different Strategies in Graph Coloring with Parallel Genetic Algorithms. International Journal of Computer Sciences and Engineering, 2(5), 138-141.

BibTex Style Citation:
@article{Sangari_2014,
author = {F.E. Sangari, M. Nabahat},
title = {Efficacy of Different Strategies in Graph Coloring with Parallel Genetic Algorithms},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2014},
volume = {2},
Issue = {5},
month = {5},
year = {2014},
issn = {2347-2693},
pages = {138-141},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=175},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=175
TI - Efficacy of Different Strategies in Graph Coloring with Parallel Genetic Algorithms
T2 - International Journal of Computer Sciences and Engineering
AU - F.E. Sangari, M. Nabahat
PY - 2014
DA - 2014/05/31
PB - IJCSE, Indore, INDIA
SP - 138-141
IS - 5
VL - 2
SN - 2347-2693
ER -

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Abstract

In this paper a new parallel genetic algorithm is proposed to observe efficacy of different strategies for k-graph coloring problem. In the algorithm we have applied a coarse-grained model of parallelism, along with two new algorithms for crossover and mutation are represented: FCX and Fmm. these algorithms compared with CEX�s First Fit and Transposi-tion mutation operators. In our experiments, we observed that different strategies what role have in finding solutions. In computer simulations of PGA we used DIMACS benchmark.

Key-Words / Index Term

Graph Coloring Problem, Migration Model, Migration Strategy, Parallel Genetic Algorithm

References

[1] Erick Cantu-Paz David E. Goldberg, �Efficient parallel genetic algorithms: theory and practice�, J. Computer Methods in Applied Mechanic and Engineering. 186, 2000, pp.221-238
[2] Zbigniew Kokosinski, Marcin Kolodziej, Krzysztof Kwarciany, �Parallel Genetic Algorithm for Graph Coloring Problem�, ICCS 2004, LNCS 3036, pp. 215�222
[3] Saeed Amizadeh, Farzad Rastegar, Caro Lucas, �Incorporating Heuristics In Evolutionary Optimization�, J. Intelligent Information Technology Computing, Vol.1, No.2, 2006, pp. 259-270
[4] Zbigniew Kokosinski, Krzysztof Kwarciany, Marcin Kolodziej, �Efficient Graph Coloring With Parallel Genetic Algorithms�, J. Computing and Informatics, Vol. 24, 2005, pp. 1001-1025
[5] Z.G. Wang, Y.S. Wong, M. Rahman, "Development of a parallel optimization method based on genetic simulated annealing algo-rithm", J. Parallel Computing, Vol. 31 ,2005, pp. 839�857
[6] Erick Cantu-Paz, "Migration Policies, takeover Times in parallel genetic algorithms", Department of computer science and Illinois Genetic Algorithm Laboratory, 1999
[7] http://mat.gsia.cmu.edu/COLOR/instances.html
[8] ftp://dimacs.rutgers.edu/pub/challenge/graph/benchmarks/
[9] http://mat.gsia.cmu.edu/COLORING03/