Evolutionary Transitions as a Metaphor for Evolutionary Optimisation

  • Anne Defaweux
  • Tom Lenaerts
  • Jano van Hemert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3630)

Abstract

This paper proposes a computational model for solving optimisation problems that mimics the principle of evolutionary transitions in individual complexity. More specifically it incorporates mechanisms for the emergence of increasingly complex individuals from the interaction of more simple ones. The biological principles for transition are outlined and mapped onto an evolutionary computation context. The class of binary constraint satisfaction problems is used to illustrate the transition mechanism.

References

  1. 1.
    Maynard Smith, J., Szathmáry, E.: The major transitions in evolution. Oxford University Press, Oxford (1995)Google Scholar
  2. 2.
    Michod, R.: Darwinian Dynamics: Evolutionary transitions in Fitness and Individuality. Princeton University Press, Princeton (1999)Google Scholar
  3. 3.
    Palmer, E.M.: Graphical Evolution. John-Wiley & Sons, New York (1985)MATHGoogle Scholar
  4. 4.
    Goldberg, D., Korb, B., Deb, K.: Messy genetic algorithms: motivation, analysis, and first results. Complex Systems 3, 493–530 (1989)MATHMathSciNetGoogle Scholar
  5. 5.
    Watson, R.A., Pollack, J.B.: Symbiotic combination as an alternative to sexual recombination in genetic algorithms. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Watson, R.A., Pollack, J.B.: A computational model of symbiotic composition in evolutionary transitions. Biosystems Special Issue on Evolvability 69(2-3), 187–209 (2002)Google Scholar
  7. 7.
    Defaweux, A., Lenaerts, T., van Hemert, J., Parent, J.: Transition models as an incremental approach for problem solving in evolutionary algorithms. In: The Genetic and Evolutionary Computation Conference (2005) (to appear)Google Scholar
  8. 8.
    Tsang, E.: Foundations of Constraint Satisfaction. Academic Press Limited, London (1993)Google Scholar
  9. 9.
    Rossi, F., Dhar, V.: On the equivalence of constraint satisfaction problems. In: Aiello, L.C. (ed.) ECAI 1990: Proceedings of the 9th European Conference on Artificial Intelligence, Stockholm, Pitman, pp. 550–556 (1990)Google Scholar
  10. 10.
    van Hemert, J.: RandomCSP (2004), Freely, available from http://freshmeat.net/projects/randomcsp/
  11. 11.
    van Hemert, J.: Application of Evolutionary Computation to Constraints Satisfaction and Data Mining. PhD thesis, Universiteit Leiden (2002)Google Scholar
  12. 12.
    Lenaerts, T., Defaweux, A., van Remortel, P., Nowé, A.: Evolutionary game dynamics of intrademic multilevel selection. Technical Report TR/IRIDIA/2005-07, IRIDIA (2005)Google Scholar
  13. 13.
    Bersini, H.: Whatever emerges should be intrinsically useful. In: Proceedings of the ninth international conference on artificial life, pp. 226–231. MIT Press, Cambridge (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Anne Defaweux
    • 1
  • Tom Lenaerts
    • 2
  • Jano van Hemert
    • 3
  1. 1.COMOVrije Universiteit BrusselBrusselsBelgium
  2. 2.IRIDIA, CP 194/6Université Libre de BruxellesBrusselsBelgium
  3. 3.Center for Emergent ComputingNapier UniversityEdinburghUnited Kingdom

Personalised recommendations