Abstract
A new combinatorial optimization algorithm, genetic-entropic algorithm, is proposed. Based on the entropic sampling, this algorithm shows better performance than the conventional genetic algorithm. We, in particular, test the algorithm using the NK-model. The higher rugged ness of the K value, the better this algorithm performs. The characteristics of the entropic sampling in this algorithm together with the difference between two algorithms are discussed.
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References
S. Kirkpatrick, C.D. Gelatt,Jr., M.P. Vecchi, “Optimization by Simulated Annealing”, Science 220 671 (1983).
J.H. Holland, Adaptation in Natural and Artificial Systems, 2nd Edition. Cambridge, MA:MIT Press (1992).
Jooyoung Lee, “New Monte Carlo Algorithm: Entropic Sampling”, Phys. Rev. Lett. 71 211 (1993).
Kang Ku Lee and Seung Kee Han, “Entropic and Boltzmann Selection in the Genetic Algorithm and Traveling Salesman Problem”, in A proceedings of International Conference on Neural Information Processing 162 (1994).
Jooyoung Lee and M.Y. Choi, “Optimization by Multicanonical Annealing and the Traveling Salesman Problem”, Phys. Rev. E 50 R651 (1994).
S.A. Kauffman, “Adaptation on Rugged Fitness Landscapes.” In Lectures in the Science of Complexity, edited by D. Stein. Santa Fe Institute Studies in the Science of Complexity, Lect. Vol.I, 527–618, Addison Wesley (1989).
E.D. Weinberger, “Local Properties of Kauffamn's NK-model”, Phys. Rev. A 44 6399 (1991).
N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, E. Teller, “Equation of State Calculations for Fast Computing Landscape”, J. Chem. Phys. 21 1087 (1953).
S.-H. Oh and Y. Lee, “A modified error function to improve the error backpropagation algorithm for Multi-Layer Perceptrons”, ETRI Journal 17 no.1, 11 (1995)
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© 1997 Springer-Verlag Berlin Heidelberg
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Leeb, CY., Han, S.K. (1997). Entropic sampling in genetic-entropic algorithm. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028521
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DOI: https://doi.org/10.1007/BFb0028521
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