On the structure and transformation of landscapes

  • Tony Hirst
Problem Structure and Fitness Landscapes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)


Whilst the metaphor of ‘fitness landscapes’ is widely applied in the evolutionary algorithm (EA) community, there are several assumptions requiring its application that are often ignored, such as the underlying structure of the search space and the ontological status of the values depicted by the landscape. By differentiating between valuation and evaluation surfaces, and surfaces of selective value, it is possible to show how each may be transformed by learning (and learning costs) and the inheritance of traits acquired therefrom. In doing so, two styles of learning are identified, rank respectful and rank transforming, and these are shown to behave differently under proportional and rank based selection schemes.


Search Space Selection Function Learning Operator Memetic Algorithm 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wright, S. (1988) “Surfaces of Selective Value Revisited.” The American Naturalist 131(1):115–123.Google Scholar
  2. 2.
    Provine, WB. (1986) Sewall Wright and Evolutionary Biology. University of Chicago Press.Google Scholar
  3. 3.
    Goldberg, DE. (1989) Genetic Algorithms in Search, Optimization & Machine Learning. Addison Wesley.Google Scholar
  4. 4.
    Byerly, HC, & Michod, RE. (1991) “Fitness and Evolutionary Explanation.” Biology and Philosophy 6:1–22.Google Scholar
  5. 5.
    Sober, E. (1984) The Nature of Selection. Bradford Books.Google Scholar
  6. 6.
    Manderick, B, de Weger, M, & Spiessens, P. (1991) The Genetic Algorithm and the Structure of the Fitness Landscape. In Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA 4), pp 143–150. Eds. RK Belew & LB Booker. Morgan Kaufmann.Google Scholar
  7. 7.
    Hirst, AJ. (1996) “Search Space Neighbourhoods as an Illustrative Device.” In Proceedings of WSCI, pp. 49–54. Nagoya University.Google Scholar
  8. 8.
    Miller, BL, & Goldberg, DE. (1996) Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise. Evolutionary Computation 4(2): 113–131.Google Scholar
  9. 9.
    Nix, AE, & Vose, MD. (1991) “Modeling Genetic Algorithms with Markov Chains”. Annals of Mathematics and Artificial Intelligence 5: 79–88.Google Scholar
  10. 10.
    Culberson, JC. (1994) “Mutation-Crossover Isomorphisms and the Construction of Discriminating Functions.” Evolutionary Computation 2(3):279–311.Google Scholar
  11. 11.
    Gitchoff, P, & Wagner, G. (1996) “Recombination Induced HyperGraphs: A New Approach to Mutation-Recombination Isomorphism.” Complxity 2:37–43.Google Scholar
  12. 12.
    Jones, T. (1994) A Model of Landscapes. Saute Fe Institute, Report SFI TR 95-0221. February 1, 1994.Google Scholar
  13. 13.
    Wagner, GP, & Altenberg, L. (1996) “Perspective-Complex Adaptations and the Evolution of Evolvability.” Evolution 50(3):967–976.Google Scholar
  14. 14.
    A elmeyer, T, Ebeling, W, & Rosffi, H. (1996) “Smoothing Representation of Fitness Landscapes–The Genotype-Phenotype Map of Evolution.” Biosystems 39(1): 63–76.Google Scholar
  15. 15.
    Radcliffe, NJ, & Surry, PD. (1994) “Formal Memetic Algorithms.” In Evolutionaly Computing: AISB Workshop. Ed. T Fogarty. Springer Verlag.Google Scholar
  16. 16.
    Whitley, D, Gordon, VS, & Mathias, K. (1994) “Lamarckian Evolution, The Baldwin Effect and Function Optimization.” In Proceedings of Parallel Problem Solving Fron Nature PPSN III, 6–15. Eds. Y Davidor, HP Schwefel & R Manner. Springer-Verlag.Google Scholar
  17. 17.
    Hinton, GE, & Nowlan, SJ. (1987) “How Learning Can Guide Evolution.” Complex Systems 1:497–502.Google Scholar
  18. 18.
    Harvey, 1. (1993) “The Puzzle of the Persistent Question Marks: A Case for Genetic Drift.” In Genetic Algorithms: Proceedings of the 5th International Conference, 15–22. Ed. S Forrest. Morgan Kaufmann.Google Scholar
  19. 19.
    Mayley, G. (1996) No Pain, No Gain: Landscapes, Learning Costs and Genetic Assimilation. CSRP 409, COGS, Sussex University. February, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Tony Hirst
    • 1
  1. 1.Dept. of PsychologyOpen UniversityMilton KeynesUK

Personalised recommendations