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Adapting to Complexity During Search in Combinatorial Landscapes

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Applications of Evolutionary Computing (EvoWorkshops 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2611))

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Abstract

Fitness landscape complexity in the context of evolutionary algorithms can be considered to be a relative term due to the complex interaction between search strategy, problem difficulty and problem representation. A new paradigm for genetic search referred to as the Collective Learning Genetic Algorithm (CLGA) has been demonstrated for combinatorial optimization problems which utilizes genotypic learning to do recombination based on a cooperative exchange of knowledge (instead of symbols) between interacting chromosomes. There is evidence to suggest that the CLGA is able to modify its recombinative behavior based on the consistency of the information in its environment, specifically, the observed fitness landscape. By analyzing the structure of the evolving individuals, a landscape-complexity measure is extracted a posteriori and then plotted for various types of example problems. This paper presents preliminary results that show that the CLGA appears to adapt its search strategy to the fitness landscape induced by the CLGA itself, and hence relative to the landscape being searched.

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References

  1. Davidor, Y. (1991). Epistasis Variance. In Foundations of Genetic Algorithms Morgan Kaufmann.

    Google Scholar 

  2. Heckendorn, R.B., Whitley, D. (1999). Walsh Functions and Predicting Problem Complexity. Evol. Comp., Vol. 7, No. 1, pp. 69–101. MIT Press.

    Article  Google Scholar 

  3. Jones, T., Forrest, S. (1995). Fitness Distance Correlation As a Measure of Problem Difficulty for Genetic Algorithms. In Proc. 6th Int. Conf. on GAs, pp. 184–192. Morgan Kaufmann.

    Google Scholar 

  4. Rose, H., Ebeling, W., Asselmeyer, T. (1996). The Density of States-a Measure of the Difficulty of Optimization Problems. In PPSN-IV, Springer-Verlag.

    Google Scholar 

  5. Reeves, C. R. (1999). Predictive Measures for Problem Difficulty. In Proc. 1999 Congress on Evol. Comp., pp. 736–743. IEEE Press.

    Google Scholar 

  6. Back, T. (1997). Self Adaptation. In The Handbook of Evolutionary Computation, pp. 1–23. IOP Publishing and Oxford University Press.

    Google Scholar 

  7. Goldberg, Korb, D.E., and Deb, K., (1989). Messy Genetic Algorithms: Motivation, Analysis, and First Results. Complex Systems, Vol. 3, pp. 493–530.

    MATH  MathSciNet  Google Scholar 

  8. Hinterding, R., Michalewicz, Z., Eiben, A.E. (1997). Adaptation in Evolutionary Computation: A Survey. In Proc. 4th Int. Conf. on Evol. Comp. pp. 65–69.

    Google Scholar 

  9. Riopka, T. P. (2002). Intelligent Recombination Using Genotypic Learning in a Collective Learning Genetic Algorithm, Doctoral dissertation, GWU, Washington, DC.

    Google Scholar 

  10. Riopka, T.P., Bock, P. (2000). Intelligent Recombination Using Individual Learning in a CLGA, In Proc. Genetic and Evol. Comp. Conf., pp. 104–111. Morgan Kaufmann.

    Google Scholar 

  11. Bock, P. (1993). The Emergence of Artificial Cognition. World Sci. Pub. Co.

    Google Scholar 

  12. De Jong, K.A. (1993). Genetic Algorithms are NOT Function Optimizers. In Foundations of Genetic Algorithms 2. Morgan Kaufmann.

    Google Scholar 

  13. DeJong, K.A., Potter, M.A., Spears, W.M. (1997). Using Problem Generators to Explore the Effects of Epistasis. In Proc. 7th Int. Conf. on Genetic Algorithms. Morgan Kaufmann.

    Google Scholar 

  14. Heckendorn, R.B., Rana, S. and Whitley, L.D. (1998). Test Function Generators as Embedded Landscapes. In Foundations of Genetic Algorithms 5, pp. 183–198. Morgan Kaufmann.

    Google Scholar 

  15. Crawford, J.A., Auton, L.D. (1996). Experimental Results on the Crossover Point in Random 3SAT. Art. Int. Vol. 81, No. 31.

    Google Scholar 

  16. Gomes, C.P., Selman, B. (2002). Satisfied with Physics. Science. Vol. 297 No. 5582.

    Google Scholar 

  17. Stephens, C.R. (1999). “Effective” Fitness Landscapes for Evolutionary Systems. In Proc. 1999 Congress on Evol. Comp., pp. 703–714. IEEE Press.

    Google Scholar 

  18. Wolpert, D.H., Macready, W.G. (1997). No Free Lunch Theorems for Optimization. IEEE Trans. Evol. Comp., vol. 1, no. 1, pp. 67–82.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering 200 Packard Lab, Lehigh University, 19 Memorial Drive West, 18015, Bethlehem, PA

    Taras P. Riopka

Authors
  1. Taras P. Riopka
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Editor information

Editors and Affiliations

  1. Dept. of Computer Engineering, University of Parma, Parco Area delle Scienze 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  2. Computing Laboratory, University of Kent, Canterbury, Kent, CT2 7NF, UK

    Colin G. Johnson

  3. Dept. of Information and Communications Technologies Faculty of Computer Science, University of A Coruña, A Coruña, CP 15071, Spain

    Juan J. Romero Cardalda

  4. Dept. of Mathematics and Computer Science, Free University of Amsterdam, de Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands

    Elena Marchiori

  5. Schol of Systems Engineering, University of Reading, PO Box 225, Whiteknights, Reading, RG6 6AY, UK

    David W. Corne

  6. AnimatLab, 8 rue du capitaine Scott, 75015, Paris, France

    Jean-Arcady Meyer  & Agnès Guillot  & 

  7. SAP AG, Neurottstrasse 16, 69190, Walldorf, Germany

    Jens Gottlieb

  8. Parallel Computing and Complex Systems Group, University of Leipzig, Augustusplatz 10/11, 04109, Leipzig, Germany

    Martin Middendorf

  9. Algorithms and Data Structures Group Institute of Computer Graphics, Vienna University of Technology, Favoritenstrasse 9-11/186, 1040, Vienna, Austria

    Günther R. Raidl

  10. School of Computing, Napier University, 219 Colinton Road, Edinburgh, EH14 1DJ, UK

    Emma Hart

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© 2003 Springer-Verlag Berlin Heidelberg

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Riopka, T.P. (2003). Adapting to Complexity During Search in Combinatorial Landscapes. In: Cagnoni, S., et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_29

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  • DOI: https://doi.org/10.1007/3-540-36605-9_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00976-4

  • Online ISBN: 978-3-540-36605-8

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