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Evolutionary Computation for Global Optimization - Current Trends

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This article comments on the development of Evolutionary Computation (EC) in the field of global optimization. A brief overview of EC fundamentals is provided together with the discussion of issues of parameter settings and adaptation, advances in the development of theory, new ideas emerging in the EC field and growing availability of massively parallel machines.
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Tom
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5--10
Opis fizyczny
Bibliogr. 41 poz.
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autor
Bibliografia
  • [1] Evolutionary Computation: The Fossil Record, D. Fogel, Ed. IEEE Press, 1998.
  • [2] K. de Jong, Evolutionary Computation. MIT Press, 2002.
  • [3] L. N. de Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach. Springer, 2002.
  • [4] S. Wierzchoń, Sztuczne Systemy Immunologiczne. Teoria i Zastosowania. EXIT, Warszawa 2001 (in Polish).
  • [5] Estimation of Distribution Algorithms: a New Tool for Evolutionary Computation, P. Larranaga and J. Lozano, Eds. Kluwer, 2002.
  • [6] M. Clerc, Particle Swarm Optimization. Wiley, 2006.
  • [7] S. Luke, “Essentials of metaheuristics”, 2009 [Online]. Available: http://cs.gmu.edu/∼sean/book/metaheuristics/
  • [8] K. Trojanowski, Metaheurystyki Praktycznie. 2nd ed. Wydawnictwo WIT, 2008 (in Polish).
  • [9] J. Arabas, Wykłady z Algorytmow Ewolucyjnych. Warszawa: WNT, 2004 (in Polish).
  • [10] A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing. 2nd ed. Springer, 2007.
  • [11] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. Springer, 1998 (3rd ed.)
  • [12] R. Horst and P. M. Pardalos, Handbook of Global Optimization. Kluwer, 1995.
  • [13] P. M. Pardalos and H. E. Romeijn, Handbook of Global Optimization, Volume 2. Kluwer, 2002.
  • [14] A. Torn and A. Zilinskas, Global Optimization. Springer, 1989.
  • [15] K. A. de Jong, “Genetic algorithms are not function optimizers”, in Proc. Worksh. Found. Genetic Algorithms FOGA 1992, Vail, Colorado, USA, 1992.
  • [16] D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization”, IEEE Trans. Evol. Comput., vol. 1, pp. 67–82, 1997.
  • [17] T. M. English, “Optimization is easy and learning is hard in the typical function”, Proc. Congr. Evol. Comp., La Jolla, CA, USA, 200, pp. 924-931.
  • [18] J. Arabas and P. Cichosz, “Searching for intelligent behavior”, in Proc. 16th Int. Conf. Intel. Inform. Sys. IIS 2008, Zakopane, Poland, pp. 3–22.
  • [19] G. Rudolph, “Convergence of evolutionary algorithms in general search spaces”, in Proc. Third IEEE Conf. Evol. Comp. CEC 1996, Nagoya, Japan, 1996, pp. 50–54.
  • [20] R. Schaefer, Foundations of Global Genetic Optimization. Springer, 2007.
  • [21] L. Dixon and G. Szeg¨o, Towards Global Optimization. North- Holland, 1975.
  • [22] N. Hansen, A. Auger, R. Ros, S. Finck, P. Posik, “Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009”, in Proc. GECCO Genetic and Evol. Comput. Conf., Portland, Oregon, USA, 2010.
  • [23] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger and S. Tiwari, “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization”, Tech. Rep., Nanyang Technological University, Singapore, 2005.
  • [24] H.-G. Beyer, The Theory of Evolution Strategies. Springer, 2001.
  • [25] G. Rudolph, “Local convergence rates of simple evolutionary algorithms with Cauchy mutations”, IEEE Trans. Evol. Comput., vol. 1, no. 4, pp. 249–258, 1997.
  • [26] M. Vose, The Simple Genetic Algorithm: Foundations and Theory. MIT Press, 1999.
  • [27] X. Qi and F. Palmieri, “Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space. Part I: Basic properties of selection and mutation”, IEEE Trans. Neural Netw., vol. 5, pp. 102–119, 1994.
  • [28] I. Karcz-Dulęba, “Dynamics of two-element populations in the space of population states”, IEEE Trans. Evol. Comput., vol. 10, pp. 199–209, 2006.
  • [29] I. Karcz-Dulęba, Dynamika Adaptacji Ewolucyjnych Metod Fenotypowych. Wrocław University of Technology Press, 2008 (in Polish).
  • [30] J. Arabas, “Approximating the genetic diversity of populations in the quasi-equilibrium state”, IEEE Trans. Evol. Comput., 2011 (to appear).
  • [31] A. Obuchowicz and P. Pretki, “Evolutionary algorithms with stable mutations based on a discrete spectral measure”, in Proc. 10th Conf. Artif. Intell. Soft Comput. LNAI, vol. 6114, part 2, pp. 181–188, Springer, 2010.
  • [32] R. Storn, “Differential evolution research – trends and open questions”, in Advances in Differential Evolution, U. K. Chakraborty, Ed. Springer, 2008, pp. 1–31.
  • [33] N. Hansen and A. Ostermeier, “Completely derandomized self-adaptation in evolution strategies”, Evol. Comput., vol. 9, pp. 159–195, 2001.
  • [34] N. Hansen, “Compilation of results on the 2005 CEC benchmark function set”, Tech. Rep., Institute of Computational Science ETH Zurich, 2006.
  • [35] A. E. Eiben, R. Hinterding, and Z. Michalewicz, “Parameter control in evolutionary algorithms”, IEEE Trans. Evol. Comput., vol. 3, pp. 124–141, 1999.
  • [36] A. E. Eiben and S. K. Smit, “Parameter tuning for configuring and analyzing evolutionary algorithms”, Swarm and Evol. Comput., vol. 1, 2011 (to appear).
  • [37] J. J. Grefenstette, “Optimization of control parameters for genetic algorithms”, IEEE Trans. Systems, Man, and Cybernetics, vol. 16, pp. 122–128, 1986.
  • [38] K. Karimi, N. G. Dickson, and F. Hamze, “A Performance Comparison of CUDA and OpenCL”, arXiv:1005.2581v3, 2011.
  • [39] O. Maitre, F. Kr ¨uger, S. Querry, N. Lachiche, and P. Collet, “EASEA: Specification and execution of evolutionary algorithms on GPGPU”, Soft Comput., vol. 1, pp. 1–19, 2011.
  • [40] T. Puzniakowski and M. Bednarczyk, “Towards an OpenCL implementation of genetic algorithms on GPUs”, in Proc. 19th Int. Conf. Intel. Inf. Systems, Warsaw, Poland, 2011 (to appear).
  • [41] R. Galar, Miękka Selekcja w Losowej Adaptacji Globalnej w Rn. Wrocław University of Technology Press, 1990 (in Polish).
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Bibliografia
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bwmeta1.element.baztech-article-BATA-0015-0012
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