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When parameter tuning actually is parameter control

Published:12 July 2011Publication History

ABSTRACT

In this paper, we show that sequential parameter optimization (SPO), a method that was designed for (offline) parameter tuning, can be successfully used as a controller for multistart approaches of evolutionary algorithms (EA). We demonstrate this by replacing the restart heuristic of the IPOP-CMA-ES with the SPO algorithm. Experiments on the BBOB 2010 test cases suggest that the performance is at least competitive while the approach provides more options, e.g. setting more than one parameter at once. Essentially, we argue that SPO is a generalization of the IPOP heuristic and that the distinction between tuning and control is---although often useful---an artificial one.

References

  1. A. Auger and N. Hansen. A restart CMA evolution strategy with increasing population size. In IEEE Congress on Evolutionary Computation (CEC'05), volume 2, pages 1769--1776. IEEE Press, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  2. T. Bartz-Beielstein, C. Lasarczyk, and M. Preuss. Sequential parameter optimization. In IEEE Congress on Evolutionary Computation (CEC'05), volume 1, pages 773--780. IEEE Press, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  3. A. Eiben, R. Hinterding, and Z. Michalewicz. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3(2):124 --141, July 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Finck, N. Hansen, R. Ros, and A. Auger. Real-parameter black-box optimization benchmarking 2009: Presentation of the noiseless functions. Technical Report 2009/20, Research Center PPE, 2009. Updated February 2010.Google ScholarGoogle Scholar
  5. N. Hansen. The CMA Evolution Strategy: A Tutorial, April 26 2008. accessed 21-01-09, http://www.bionik.tu-berlin.de/user/niko/cmatutorial.pdf.Google ScholarGoogle Scholar
  6. N. Hansen. Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. In Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference, pages 2389--2395. ACM, July 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. N. Hansen. CMA-ES implementation in Python. online, November 2010. http://www.lri.fr/ hansen/cma.py.Google ScholarGoogle Scholar
  8. N. Hansen, A. Auger, S. Finck, and R. Ros. Real-parameter black-box optimization benchmarking 2010: Experimental setup. Technical Report RR-7215, INRIA, 2010.Google ScholarGoogle Scholar
  9. N. Hansen and A. Ostermeier. Completely Derandomized Self-Adaptation in Evolution Strategies. Evolutionary Computation, 9(2):159--195, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. H. Hoos and T. Stützle. Stochastic Local Search -- Foundations and Applications. Morgan Kaufmann, San Francisco, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. N. Lophaven, H. B. Nielsen, and J. Søndergaard. DACE -- A Matlab Kriging Toolbox. Technical Report IMM-REP-2002--12, Informatics and Mathematical Modelling, Technical University of Denmark, Copenhagen, Denmark, August 2002.Google ScholarGoogle Scholar
  12. M. Lunacek, D. Whitley, and A. Sutton. The impact of global structure on search. In Parallel Problem Solving from Nature -- PPSN X, volume 5199 of Lecture Notes in Computer Science, pages 498--507. Springer Berlin / Heidelberg, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. D. C. Montgomery. Design and Analysis of Experiments. Wiley, New York, 4th edition, 1997.Google ScholarGoogle Scholar
  14. M. Preuss, G. Rudolph, and S. Wessing. Tuning optimization algorithms for real-world problems by means of surrogate modeling. In Genetic and Evolutionary Computation Conference (GECCO), pages 401--408, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. K. Price. Differential evolution vs. the functions of the second ICEO. In Proceedings of the IEEE International Congress on Evolutionary Computation, pages 153--157, 1997.Google ScholarGoogle Scholar
  16. R. Ros. Black-box optimization benchmarking the IPOP-CMA-ES on the noiseless testbed: comparison to the BIPOP-CMA-ES. In Proceedings of the 12th annual conference companion on Genetic and evolutionary computation, GECCO '10, pages 1503--1510. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. Sacks, W. J. Welch, T. J. Mitchell, and H. P. Wynn. Design and analysis of computer experiments. Statistical Science, 4(4):409--423, November 1989.Google ScholarGoogle ScholarCross RefCross Ref
  18. T. J. Santner, B. J. Williams, and W. I. Notz. The Design and Analysis of Computer Experiments. Springer, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  19. K. Sastry. Single and multiobjective genetic algorithm toolbox for matlab in cGoogle ScholarGoogle Scholar
  20. . Technical Report 2007017, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, 2007.Google ScholarGoogle Scholar
  21. H.-P. Schwefel. Evolution and Optimum Seeking. Wiley, New York, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. R. G. Steel and J. H. Torrie. Principles and Procedures of Statistics. McGraw-Hill, 1960.Google ScholarGoogle Scholar
  23. S. Wessing and T. Wagner. A rank transformation can improve sequential parameter optimization. Algorithm Engineering Report TR10--2-007, Technische Universitat Dortmund, 2010.Google ScholarGoogle Scholar
  24. K. Zielinski and R. Laur. Stopping Criteria for a Constrained Single-Objective Particle Swarm Optimization Algorithm. Informatica, 31(1):51--59, 2007.Google ScholarGoogle Scholar

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        cover image ACM Conferences
        GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
        July 2011
        2140 pages
        ISBN:9781450305570
        DOI:10.1145/2001576

        Copyright © 2011 ACM

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        Publication History

        • Published: 12 July 2011

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