Performance of population size on Knapsack problem

Yıl 2016, Cilt: 4 Sayı: 4, 253 - 258, 31.12.2016

David Oyewola , Danladi Hakimi Amuda Yusuph Yahaya Gbolahan Bolarin

Öz

In order to obtain meaningful information about the
performance of the population size, a considerable number of independent runs
of the GA are performed. Accurate model parameters values are obtained in
reasonable computational time. Further increase of the population size, does
not improve the solution accuracy. Moreover, the computational time is
increased significantly.

Anahtar Kelimeler

Population, parameter, chromosome, genetic algorithm

Kaynakça

• C. R. Reeves, Using Genetic Algorithms With Small Populations,In Proceedings of the Fifth International Conference on Genetic Algorithms, (1993), pp. 92-99.
• O. Roeva and Ts. Slavov, Fed-batch Cultivation Control based on Genetic Algorithm PID Controller Tuning, Lecture Notes on Computer Science, Springer-Verlag Berlin Heidelberg, Vol. 6046, (2011), pp. 289-296.
• V. K. Koumousis and C. P. Katsaras, A sawtooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance, IEEE Transactions on Evolutionary Computation,Vol. 10, No. 1, (2006), pp. 19–28.
• M. Pelikan, D. E. Goldberg, and E. Cantu-Paz, Bayesian optimization algorithm, population sizing, and time to convergence, Illinois Genetic Algorithms Laboratory, University of Illinois, Tech. Rep., (2000).
• A. Piszcz and T. Soule, Genetic programming: Optimal population sizes for varying complexity problems, In Proceedings of the Genetic and Evolutionary Computation Conference, (2006), pp. 953–954
• F. G. Lobo and D. E. Goldberg, The parameterless genetic algorithm in practice, Information Sciences Informatics and Computer Science, Vol. 167, No. 1-4, (2004), pp. 217–232.
• F. G. Lobo and C. F. Lima, A review of adaptive population sizing schemes in genetic algorithms, In Proceedings of the Genetic and Evolutionary, Computation Conference, (2005), pp. 228–234.
• S.Akpinar and G.M. Bayhan, A Hybrid Genetic Algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints, Engineering Applications of Artificial intellligence. Vol. 24, No 3 (2011), pp. 449-457
• H.N.Al-Duwaish, A Genetic Approach to the Identification of Linear Dynamic Systems with Static Nonlinearities. International Journal of Systems Science, Vol. 31, No. 3, 2000, pp. 307-313.
• J.P.Paplinksi, The Genetic Algorithm with Simplex Crossover for Identification of Time Delays, Intelligent Information Systems, 2010, pp. 337-346.
• M. Arndt and B.Hitzmann, Feed Forward/Feed back Control of Glucose Concentration during Cultivation of Escherichia coli, 8 ^th IFAC Int. Conf. on Comp. Appl. in Biotechn, Canada, 2001, pp. 425-429.
• Kaushik Kumar Bhattacharjee, S.P.Sarmah, Shuffled frog leaping algorithm and its application to 0/1 Knapsack problem, Applied Soft Computing, 19(2014) 252-263
• Vasquez, M. & Hao, J. K. A logic-constrained knapsack formulation and a tabu algorithm for the daily photograph scheduling of an earth observation satellite.Comput. Optim. Appl.,20, 137–157, (2001).
• Vincent, Boyer, Didier, El Baz and Moussa Elkihel, Solution of multidimensional knapsack problems via cooperation of dynamic programming and branch and bound, European J. Industrial Engineering, Vol. 4, No. 4,(2010).
• Wagner, H.M., Principles of Operations Research, Prentic-Hall, Inc,(1969).
• Yamada, T. & Futakawa, M. Heuristic and reduction algorithms for the knapsack sharing problem. Comput. Oper. Res.,24, 961–967,(1997).