The Challenge of Complexity

  • Wolfgang Banzhaf
  • Julian Miller
Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 11)


In this chapter we discuss the challenge provided by the problem of evolving large amounts of computer code via Genetic Programming. We argue that the problem is analogous to what Nature had to face when moving to multi-cellular life. We propose to look at developmental processes and there mechanisms to come up with solutions for this “challenge of complexity” in Genetic Programming.


Genetic Programming Evolutionary Algorithm Complexity Scaling Problem Development Heterochrony 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Angeline, P. and Pollack, J. (1994). Coevolving high-level representations. In Langton, C., editor, Proc. Artificial Life III, pages 55–71, Reading, MA. Addison Wesley.Google Scholar
  2. Arnone, M. (2002). Bringing Order to Organogenesis. Nature Genetics, 30:348–350.CrossRefGoogle Scholar
  3. Banzhaf, W. (2002). Self-Organizing Systems, volume 14 of Encyclopedia of Physical Science and Technology, pages 589–598. Academic Press, New York.Google Scholar
  4. Banzhaf, W., Banscherus, D., and Dittrich, P. (1999). Hierarchical Genetic Programming using local modules. Technical Report CI-56/99 of SFB 531, University of Dortmund.Google Scholar
  5. Banzhaf, W., Nordin, P., Keller, R., and Francone, F. D. (1998). Genetic Programming-An Introduction. Morgan Kaufmann, San Francisco, CA.Google Scholar
  6. Brameier, M. (2003). Linear Genetic Programming. PhD thesis, Department of Computer Science, University of Dortmund. (to appear).Google Scholar
  7. Calow, P. (1976). Biological Machines: A cybernetic approach to life. E. Arnold, London.Google Scholar
  8. Cangelosi, A. (1999). Heterochrony and adaptation in developing neural networks. In W. Banzhaf et al., editor, Proceedings of GECCO99 Genetic and Evolutionary Computation Conference, pages 1241–1248, San Francisco, CA. Morgan Kaufmann.Google Scholar
  9. Davidson, E. H. (2001). Genomic Regulatory Systems. Academic Press, San Diego.Google Scholar
  10. Gaudet, J. and Mango, S. E. (2002). Regulation of Organogenesis by the Caenorhabditis elegans FoxA Protein PHA-4. Science, 295:821–825.CrossRefGoogle Scholar
  11. Gould, S. J. (1977). Ontogeny and Phylogeny. Belknap Press of Harvard University Press, Cambridge, MA.Google Scholar
  12. Gould, S. J. (1980). The Evolutionary Biology of Constraint. Daedalus, 109:39–52.Google Scholar
  13. Gould, S. J. (2002). The Structure of Evolutionary Theory. Belknap Press of Harvard University Press, Cambridge, MA.Google Scholar
  14. Gruau, F. (1993). Genetic Synthesis of Modular Neural Networks. In Forrest, S., editor, Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, pages 318–325, San Francisco, CA. Morgan Kaufmann.Google Scholar
  15. Haeckel, E. (1866). Generelle Morphologie der Organismen. Reimer, Berlin.Google Scholar
  16. Harold, F. (2001a). Gleanings of a chemiosmotic eye. Bioessays, 21:848–855.Google Scholar
  17. Harold, F. (2001b). The Way of the Cell. Oxford University Press, Oxford.Google Scholar
  18. Kargupta, H. (2002). Editorial: Computation in Gene Expression. Genetic Programming and Evolvable Machines, 3:111–112.Google Scholar
  19. Kennedy, P. J. and Osborn, T. R. (2001). A Model of Gene Expression and Regulation in an Artificial Cellular Organism. Complex Systems, 13.Google Scholar
  20. Koza, John R. (1992). Genetic Programming. MIT Press, Cambridge, MA.Google Scholar
  21. Koza, John R. (1994). Genetic Programming II. MIT Press, Cambridge, MA.Google Scholar
  22. Langdon, W. B. (1999a). Boolean function fitness spaces. In Poli, R., Nordin, P., Langdon, W. B., and Fogarty, T., editors, Proceedings EuroGP’99, Berlin. Springer.Google Scholar
  23. Langdon, W. B. (1999b). Scaling of Program Tree Fitness Spaces. Evolutionary Computation, 7:399–428.Google Scholar
  24. McKinney, M. (1999). Heterochrony: Beyond words. Paleobiology, 25:149–153.Google Scholar
  25. McKinney, M. and McNamara, K. (1991). Heterochrony: The Evolution of Ontogeny. Plenum Press, New York.Google Scholar
  26. Mushegian, A. and Koonin, E. (1996). A minimal gene set for cellular life derived by comparison of complete bacterial genomes. Proc. Natl. Acad. Sci. (USA), 93:10268–73.Google Scholar
  27. Neidhardt, F. C. (1996). Escherichia Coli and Salmonella typhimurium. ASM Press, Washington, DC.Google Scholar
  28. Rosca, J. and Ballard, D. (1994). Hierarchical selforganization in genetic programming. In Proc. of the 11th Int. Conf. on Machine Learning, pages 252–258, San Mateo, CA. Morgan Kaufmann.Google Scholar
  29. Rosen, R. (1994). Life Itself. Columbia University Press, New York.Google Scholar
  30. Smith, T., Husbands, P., and O’Shea, M. (2001). Neutral Networks and Evolvability with Complex Genotype-Phenotype mapping. In Kemelen, E. and Socik, S., editors, Proc. 6th ECAL-01, Prague, 2001, pages 272–281, Berlin. Springer.Google Scholar
  31. Thomas, G. H. (1999). Completing the E. coli proteome: a database of gene products characterised since completion of the genome sequence. Bioinformatics, 7:860–861.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Wolfgang Banzhaf
    • 1
  • Julian Miller
    • 2
  1. 1.Department of Computer ScienceUniversity of DortmundGermany
  2. 2.School of Computer ScienceThe University of BirminghamUK

Personalised recommendations