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AH Method: a Novel Routine for Vicinity Examination of the Optimum Found with a Genetic Algorithm

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents a novel heuristic procedure (further called the AH Method) to investigate function shape in the direct vicinity of the found optimum solution. The survey is conducted using only the space sampling collected during the optimization process with an evolutionary algorithm. For this purpose the finite model of point-set is considered. The statistical analysis of the sampling quality based upon the coverage of the points in question over the entire attraction region is exploited. The tolerance boundaries of the parameters are determined for the user-specified increase of the objective function value above the found minimum. The presented test-case data prove that the proposed approach is comparable to other optimum neighborhood examination algorithms. Also, the AH Method requires noticeably shorter computational time than its counterparts. This is achieved by a repeated, second use of points from optimization without additional objective function calls, as well as significant repository size reduction during preprocessing.
Rocznik
Strony
695--708
Opis fizyczny
Bibliogr. 31 poz., rys., tab., wykr.
Twórcy
  • Institute of Radioelectronics and Multimedia Technology, Warsaw University of Technology, Poland
autor
  • Institute of Radioelectronics and Multimedia Technology, Warsaw University of Technology, Poland
  • Heavy Ion Laboratory, University of Warsaw, Poland
Bibliografia
  • [1] A. P. Engelbrecht: Computational Intelligence, John Wiley & Sons Ltd., 2002.
  • [2] H.-P. Schwefel: Numerical optimization of computer models, Chichester:
  • Wiley & Sons, 1981.
  • [3] D. E. Goldberg: Genetic Algorithms in Search, Optimization and Machine
  • Learning, Kluwer Academic Publishers, Boston, MA, 1989.
  • [4] Z. Michalewicz: Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, 1999.
  • [5] Z. Michalewicz, D. B. Fogel: How to Solve It: Modern Heuristics, Springer Berlin, Heidelberg, 2004.
  • [6] J. Arabas: Approximating the Genetic Diversity of Populations in the Quasi-Equilibrum State, IEEE Transactions on Evolutionary Computation, 16, 5, 2012, https://doi.org/10.1109/TEVC.2011.2166157.
  • [7] D. A. Piętak, P. J. Napiorkowski, Z. Walczak, J. Wojciechowski: Application of Genetic Algorithm with Real Representation to COULEX Data Analysis, Proc. Conference on Evolutionary Computation and Global Optimization, 2009.
  • [8] M. Ankerst, M. Breunig, H.-P. Kriegel, J. Sander: OPTICS: Ordering points to identify the clustering structure, Proc. 1999 ACM-SIGMOD International Conference on Management of Data (SIGMOD'99), pp. 49-60, Philadelphia, 1999, https://doi.org/10.1145/304181.304187.
  • [9] M. Ester, H. Kriegel, J. Sander, X. Xu: A density-based algorithm for discovering clusters in large spatial databases with noise, Proc. International Conference on Knowledge Discovery and Data Mining (KDD'96), pp. 226-231, Portland Oregon, 1998.
  • [10] J. Han, M. Kamber: Data mining: concepts and techniques, Morgan Kaufmann, 2000, https://doi.org/10.1016/C2009-0-61819-5.
  • [11] A. K. Jain, M. N. Murty, P. J. Flyn: Data clustering: A review, ACM Computing Surveys, 31(3): 264-323, 1999, https://doi.org/10.1145/331499.331504.
  • [12] R. Weber, H.-J. Schek, S. Blott: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces, Proc. Conference on Very Large DataBases (VLDB'98), pp. 194-205, New York City, USA, 1998.
  • [13] R. Weber, S. Blott: An approximation based data structure for similarity search, Technical Report 24, ESPRIT project HERMES (no. 9141), 1997.
  • [14] S. Zhou, Y. Zhao, J. Guan, J. Huang: NBC: A Neighborhood-Based Clustering Algorithm, Lecture Notes in Computer Science, Springer Berlin/Heidelberg, 2005, https://doi.org/10.1007/11430919_43.
  • [15] T. Kohonen: Self-Organized Formation of Topologically Correct Feature Maps, Biological Cybernetics, 43(1), pp. 59-69, 1982, https://doi.org/10.1007/BF00337288.
  • [16] J. E. Amaro, R. Navarro Pérez, E. Ruiz Arriola: Error analysis of nuclear matrix elements, Few-Body Systems, 2013, https://doi.org/10.1007/s00601-013-0756-4.
  • [17] P. R. Bevington, D. K. Robinson: Data Reduction and Error Analysis for the Physical Sciences, McGraw-Hill, 2003.
  • [18] G. E. P. Box, N. R. Draper: Empirical Model Building and Response Surfaces, John Wiley & Sons, New York, 1987.
  • [19] S. Brandt: Data Analysis. Statistical and Computational Methods for Scientists and Engineers, Fourth Edition, Springer, New York, 2014.
  • [20] International Organization for Standardization (ISO): Guide to the expression of Uncertainty in Measurement (GUM) - Supplement 1: Numerical methods for the propagation of distributions, Report, 2004.
  • [21] Joint Committee for Guides in Metrology (JCGM): JCGM 100:2008 Evaluation of measurement data - Guide to the expression of Uncertainty in Measurement, Report, 2008.
  • [22] R. Z. Morawski, A. Miękina: Monte-Carlo evaluation of measurement uncertainty using a new generator of pseudo-random numbers, PAK vol. 59, nr 5, 2013.
  • [23] A. Saltelli, S. Funtowicz: When all models are wrong, Issues in Science and Technology,:79, 2013.
  • [24] J. R. Taylor: An Introduction to Error Analysis, Oxford University Press, 1982.
  • [25] B. A. Wichmann, I. D. Hill: Generating good pseudo-random numbers, Computational Statistics and Data Analysis, 51, pp. 1614-1622, 2006, https://doi.org/10.1016/j.csda.2006.05.019.
  • [26] B. M. Adams, W. J. Bohnhoff, K. R. Dalbey, J. P. Eddy, M. S. Eldred, D. M. Gay, K. Haskell, P. D. Hough, L. P. Swiler: DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification and Sensitivity Analysis: Version 5.0 User’s Manual, Sandia Technical Report, SAND2010-2183, 2009.
  • [27] C. B. Moler: Numerical computing with MATLAB, SIAM, Philadelphia, 2004, https://doi.org/10.1137/1.9780898717952.
  • [28] T. Williams, C. Kelley: Gnuplot 5.3. An Interactive Plotting Program, 2018, in: http://www.gnuplot.info/docs_5.5/gnuplot.pdf, access: August 2022.
  • [29] D. A. Piętak: Statistical distribution of the genetic algorithm sampling with Schwefel's F7 objective function, Proc. IEEE International Conference on Signals and Electronic Systems (ICSES), Gliwice, Poland, 2010.
  • [30] D. A. Piętak, J. Wojciechowski, P. J. Napiorkowski: A Front-Line algorithm for error estimation in datasets with nonuniform sampling distribution, Proc. 20th European Conference on Circuit Theory and Design (ECCTD), 6043319, pp. 210-213, Linköping, Sweden, 2011.
  • [31] D. Cline, T. Czosnyka, A. B. Hayes, P. J. Napiorkowski, N. Warr, C. Y. Wu: GOSIA User Manual for Simulation and Analysis of Coulomb Excitation Experiments, http://www.pas.rochester.edu/~cline/Gosia/Gosia_Manual_20120510.pdf, 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-154d1d32-f1ca-4333-b976-ec75495b3a5a
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