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All issues Volume 210 (2018) MATEC Web Conf., 210 (2018) 04052 References
Open Access
Issue
MATEC Web Conf.
Volume 210, 2018
22nd International Conference on Circuits, Systems, Communications and Computers (CSCC 2018)
Article Number 04052
Number of page(s) 5
Section Computers
DOI https://doi.org/10.1051/matecconf/201821004052
Published online 05 October 2018
  1. C. Blum, & A. Roli, Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM computing surveys (CSUR), 35(3), 268-308. (2003) [CrossRef] [Google Scholar]
  2. C. Solnon, & D. Bridge, An ant colony optimization metaheuristic for subset selection problems. In: Nedjah, N., Mourelle, L.M. (eds.) Systems Engineering Using Particle Swarm Optimization, pp. 3-25. Nova Science Publishers, New York, (2006) [Google Scholar]
  3. S. Fidanova, Ant colony optimization and multiple knapsack problem. In: JP. Rennard, (Ed.), Handbook of research on nature inspired computing for economics and management. pp. 498-509, Idea Group, (2007) [CrossRef] [Google Scholar]
  4. V. Maniezzo, & M. Roffilli, Very strongly constrained problems: an ant colony optimization approach. Cybernetics and Systems: An International Journal, 39(4), 395-424. (2008) [CrossRef] [Google Scholar]
  5. G. Luque, & E. Alba, Parallel genetic algorithms: Theory and real world applications (Vol. 367). Springer. (2011) [Google Scholar]
  6. N. Melab, & E. G. Talbi, GPU-based island model for evolutionary algorithms. In Proceedings of the 12th annual conference on Genetic and evolutionary computation (pp. 1089-1096). ACM. (2010, July) [Google Scholar]
  7. M. Dorigo, T. Stutzle, Ant Colony Optimization. MIT Press, Cambridge, (2004) [Google Scholar]
  8. C. Blum, Ant colony optimization: Introduction and recent trends. Physics of Life reviews, 2(4), 353-373. (2005) [Google Scholar]
  9. N. Abd-Alsabour, Hybrid Metaheuristics for Classification Problems, In: S. Ramakrishnan, (Ed.). Pattern Recognition - Analysis and Applications, InTech, (2016) [Google Scholar]
  10. V. Maniezzo, and M. Milandri, An Ant-Based Framework for Very Strongly Constrained Problems, in M. Dorigo, et al. (Eds.): Ants 2002, LNCS, vol. 2463, pp. 222-227, Springer, Berlin, Heidelberg (2002) [Google Scholar]
  11. H. Mühlenbein, Parallel genetic algorithms in combinatorial optimization. In O. Balci, (Ed.) Computer Science and Operations Research: New Developments in Their Interfaces. pp. 441-453, Elsevier. (2014) [Google Scholar]
  12. E. Cantú-Paz, A survey of parallel genetic algorithms. Calculateurs paralleles, reseaux et systems repartis, 10(2), 141-171. (1998) [Google Scholar]
  13. C. C. Li, C. H. Lin, & J. C. Liu, Parallel genetic algorithms on the graphics processing units using island model and simulated annealing. Advances in Mechanical Engineering, 9 (7), 1687814017707413. (2017) [Google Scholar]
  14. O. Roeva, S. Fidanova, & M. Paprzycki, Influence of the population size on the genetic algorithm performance in case of cultivation process modelling. In Computer Science and Information Systems (FedCSIS), 2013 Federated Conference on (pp. 371-376). IEEE. (2013, September) [Google Scholar]
  15. Z. Konfrst, Parallel genetic algorithms: Advances, computing trends, applications and perspectives. In Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International (p. 162). IEEE. (2004, April) [Google Scholar]
  16. A. P. Shukla, R. Tiwari, & R. Kala, Real life applications of soft computing. CRC press. (2010) [CrossRef] [Google Scholar]
  17. J. Yang, & V. Honavar, Feature subset selection using a genetic algorithm. In Feature extraction, construction and selection (pp. 117-136). Springer, Boston, MA. (1998) [CrossRef] [Google Scholar]
  18. KNAPSACK0-1 - Data for the 0-1 Knapsack Problems. Available at:https://people.sc.fsu.edu/~jburkardt/datasets/knapsack_01/ knapsack_01.html. Last visited on 30-6-2018 [Google Scholar]
  19. A. Syarif, Aristoteles, A. Dwiastuti, and R. Malinda, Performance Evaluation of Various Genetic Algorithm Approaches for Knapsack Problem. ARPN Journal of Engineering and Applied Sciences. 11(7), 4713-4719. (2016, April) [Google Scholar]
  20. A. Güler, M. Berberler, U. Nuriyev, A New Genetic Algorithm for the 0-1 Knapsack Problem. Akademik Platform Mühendislik ve Fen Bilimleri Dergisi, 4(3), 9-14. DOI: 10.21541/apjes.14020. (2016) [Google Scholar]
  21. R: A Language and Environment for Statistical Computing [http://www.R-project.org]. R Foundation for Statistical Computing, Vienna, Austria. [Google Scholar]
  22. A. Bondarenko, On Application of the Local Search and the Genetic Algorithms Techniques to Some Combinatorial Optimization Problems. arXiv preprint arXiv:1004.5262. (2010) [Google Scholar]

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