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Fitness Landscape Based Parameter Estimation for Robust Taboo Search

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8111)

Introduction

Metaheuristic optimization algorithms are general optimization strategies suited to solve a range of real-world relevant optimization problems. Many metaheuristics expose parameters that allow to tune the effort that these algorithms are allowed to make and also the strategy and search behavior [1]. Adjusting these parameters allows to increase the algorithms’ performances with respect to different problem- and problem instance characteristics.

Keywords

  • Problem Instance
  • Problem Size
  • Fitness Landscape
  • Quadratic Assignment Problem
  • Large Problem Size

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Affenzeller, M., Winkler, S., Wagner, S., Beham, A.: Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Numerical Insights. CRC Press (2009)

    Google Scholar 

  2. Bischl, B., Mersmann, O., Trautmann, H., Preuss, M.: Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2012), pp. 313–320 (2012)

    Google Scholar 

  3. Chicano, F., Luque, G., Alba, E.: Autocorrelation measures for the quadratic assignment problem. Applied Mathematics Letters 25, 698–705 (2012)

    MathSciNet  CrossRef  MATH  Google Scholar 

  4. Glover, F.: Tabu search – part I. ORSA Journal on Computing 1(3), 190–206 (1989)

    CrossRef  MATH  Google Scholar 

  5. Koopmans, T.C., Beckmann, M.: Assignment problems and the location of economic activities. Econometrica, Journal of the Econometric Society 25(1), 53–76 (1957)

    MathSciNet  CrossRef  MATH  Google Scholar 

  6. Pitzer, E., Affenzeller, M.: A Comprehensive Survey on Fitness Landscape Analysis. In: Fodor, J., Klempous, R., Araujo, C.P.S. (eds.) Recent Advances in Intelligent Engineering Systems. SCI, vol. 378, pp. 161–191. Springer, Heidelberg (2011)

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  7. Pitzer, E., Beham, A., Affenzeller, M.: Generic hardness estimation using fitness and parameter landscapes applied to robust taboo search and the quadratic assignment problem. In: Companion Publication of the 2012 Genetic and Evolutionary Computation Conference, pp. 393–400 (2012)

    Google Scholar 

  8. Taillard, E.D.: Robust taboo search for the quadratic assignment problem. Parallel Computing 17, 443–455 (1991)

    MathSciNet  CrossRef  Google Scholar 

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Beham, A., Pitzer, E., Affenzeller, M. (2013). Fitness Landscape Based Parameter Estimation for Robust Taboo Search. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2013. EUROCAST 2013. Lecture Notes in Computer Science, vol 8111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53856-8_37

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  • DOI: https://doi.org/10.1007/978-3-642-53856-8_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53855-1

  • Online ISBN: 978-3-642-53856-8

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