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When parameter tuning actually is parameter control

Published: 12 July 2011 Publication 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.
[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.
[3]
A. Eiben, R. Hinterding, and Z. Michalewicz. Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 3(2):124 --141, July 1999.
[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.
[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.
[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.
[7]
N. Hansen. CMA-ES implementation in Python. online, November 2010. http://www.lri.fr/ hansen/cma.py.
[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.
[9]
N. Hansen and A. Ostermeier. Completely Derandomized Self-Adaptation in Evolution Strategies. Evolutionary Computation, 9(2):159--195, 2001.
[10]
H. Hoos and T. Stützle. Stochastic Local Search -- Foundations and Applications. Morgan Kaufmann, San Francisco, 2004.
[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.
[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.
[13]
D. C. Montgomery. Design and Analysis of Experiments. Wiley, New York, 4th edition, 1997.
[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.
[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.
[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.
[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.
[18]
T. J. Santner, B. J. Williams, and W. I. Notz. The Design and Analysis of Computer Experiments. Springer, 2003.
[19]
K. Sastry. Single and multiobjective genetic algorithm toolbox for matlab in c
[20]
. Technical Report 2007017, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, 2007.
[21]
H.-P. Schwefel. Evolution and Optimum Seeking. Wiley, New York, 1995.
[22]
R. G. Steel and J. H. Torrie. Principles and Procedures of Statistics. McGraw-Hill, 1960.
[23]
S. Wessing and T. Wagner. A rank transformation can improve sequential parameter optimization. Algorithm Engineering Report TR10--2-007, Technische Universitat Dortmund, 2010.
[24]
K. Zielinski and R. Laur. Stopping Criteria for a Constrained Single-Objective Particle Swarm Optimization Algorithm. Informatica, 31(1):51--59, 2007.

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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
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2011

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Author Tags

  1. CMA-ES
  2. SPO
  3. parameter control
  4. parameter setting
  5. parameter tuning

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Cited By

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  • (2017)Multimodal optimization by covariance matrix self-adaptation evolution strategy with repelling subpopulationsEvolutionary Computation10.1162/evco_a_0018225:3(439-471)Online publication date: 1-Sep-2017
  • (2017)Finite life span for improving the selection scheme in evolution strategiesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-015-1805-321:2(501-513)Online publication date: 1-Jan-2017
  • (2014)A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search AlgorithmsApplications of Evolutionary Computation10.1007/978-3-662-45523-4_50(615-626)Online publication date: 29-Nov-2014
  • (2013)EA-based parameter tuning of multimodal optimization performance by means of different surrogate modelsProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2482684(1063-1070)Online publication date: 6-Jul-2013
  • (2013)Taxonomy of Evolution StrategiesContemporary Evolution Strategies10.1007/978-3-642-40137-4_3(47-54)Online publication date: 18-Aug-2013
  • (2013)Evolution StrategiesContemporary Evolution Strategies10.1007/978-3-642-40137-4_2(7-45)Online publication date: 18-Aug-2013
  • (2013)Experimental Analysis of Optimization Algorithms: Tuning and BeyondTheory and Principled Methods for the Design of Metaheuristics10.1007/978-3-642-33206-7_10(205-245)Online publication date: 12-Nov-2013

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