Advertisement

Evolutionary Optimization in Spatio–temporal Fitness Landscapes

  • Hendrik Richter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)

Abstract

Spatio–temporal fitness landscapes that are constructed from Coupled Map Lattices (CML) are introduced. These landscapes are analyzed in terms of modality and ruggedness. Based on this analysis, we study the relationship between landscape measures and the performance of an evolutionary algorithm used to solve the dynamic optimization problem.

Keywords

Evolutionary Algorithm Correlation Length Evolutionary Optimization Dynamic Optimization Fitness Landscape 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford Univ. Press, NY (1996)MATHGoogle Scholar
  2. 2.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  3. 3.
    Chazottes, J.R., Fernandez, B.: Dynamics of Coupled Map Lattices and of Related Spatially Extended Systems. Springer, Berlin (2005)MATHGoogle Scholar
  4. 4.
    Hordijk, W.: A Measure of Landscapes. Evolut. Comput. 4, 335–360 (1996)CrossRefGoogle Scholar
  5. 5.
    Hordijk, W., Kauffman, S.A.: Correlation Analysis of Coupled Fitness Landscapes. Complexity 10, 42–49 (2005)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Jin, Y., Branke, J.: Evolutionary Optimization in Uncertain Environments–A Survey. IEEE Trans. Evolut. Comput. 9, 303–317 (2005)CrossRefGoogle Scholar
  7. 7.
    Kallel, L., Naudts, B., Reeves, C.R.: Properties of Fitness Functions and Search Landscapes. In: Kallel, L., et al. (eds.) Theoretical Aspects of Evolutionary Computing, pp. 177–208. Springer, Berlin (2001)Google Scholar
  8. 8.
    Kaneko, K.: The Coupled Map Lattice. In: Kaneko, K. (ed.) Theory and Application of Coupled Map Lattices, pp. 1–49. John Wiley, Chichester (1993)Google Scholar
  9. 9.
    Morrison, R.W.: Designing Evolutionary Algorithms for Dynamic Environments. Springer, Berlin (2004)MATHGoogle Scholar
  10. 10.
    Papadimitriou, C.H., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity, Dover, Mineola (1998)Google Scholar
  11. 11.
    Richter, H.: Behavior of Evolutionary Algorithms in Chaotically Changing Fitness Landscapes. In: Yao, X., et al. (eds.) Parallel Problem Solving from Nature–PPSN VIII, pp. 111–120. Springer, Berlin (2004)CrossRefGoogle Scholar
  12. 12.
    Richter, H.: A Study of Dynamic Severity in Chaotic Fitness Landscapes. In: Corne, D. (ed.) Proc. Congress on Evolutionary Computation, IEEE CEC 2005, pp. 2824–2831. IEEE Press, Piscataway (2005)CrossRefGoogle Scholar
  13. 13.
    Shibata, T., Kaneko, K.: Coupled Map Gas: Structure Formation and Dynamics of Interacting Mobile Elements with Internal Dynamics. Phys. D181, 197–214 (2003)Google Scholar
  14. 14.
    Smith, T., Husbands, P., Layzell, P., O’Shea, M.: Fitness Landscapes and Evolvability. Evolut. Comput. 10, 1–34 (2002)CrossRefGoogle Scholar
  15. 15.
    Stadler, P.F.: Landscapes and Their Correlation Functions. J. Math. Chem. 20, 1–45 (1996)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Stadler, P.F., Stephens, C.R.: Landscapes and Effective Fitness. Comm. Theor. Biol. 8, 389–431 (2003)CrossRefGoogle Scholar
  17. 17.
    Weicker, K.: An Analysis of Dynamic Severity and Population Size. In: Schoenauer, M., et al. (eds.) Parallel Problem Solving from Nature–PPSN VI, pp. 159–168. Springer, Berlin (2000)CrossRefGoogle Scholar
  18. 18.
    Weinberger, E.D.: Correlated and Uncorrelated Fitness Landscapes and How to Tell the Difference. Biol. Cybern. 63, 325–336 (1990)MATHCrossRefGoogle Scholar
  19. 19.
    Yang, S., Yao, X.: Experimental Study on Population-based Incremental Learning Algorithms for Dynamic Optimization Problems. Soft Comput. 9, 815–834 (2005)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hendrik Richter
    • 1
  1. 1.Fachbereich Elektrotechnik und Informationstechnik, Institut Mess–, Steuerungs– und RegelungstechnikHTWK LeipzigLeipzigGermany

Personalised recommendations