Finding Needles in Haystacks Is Not Hard with Neutrality

  • Tina Yu
  • Julian Miller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2278)


We propose building neutral networks in needle-in-haystack fitness landscapes to assist an evolutionary algorithm to perform search. The experimental results on four different problems show that this approach improves the search success rates in most cases. In situations where neutral networks do not give performance improvement, no impairment occurs either. We also tested a hypothesis proposed in our previous work. The results support the hypothesis: when the ratio of adaptive/neutral mutations during neutral walk is close to the ratio of adaptive/neutral mutations at the fitness improvement step, the evolutionary search has a high success rate. Moreover, the ratio magnitudes indicate that more neutral mutations (than adaptive mutations) are required for the algorithms to find a solution in this type of search space.


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  1. [1]
    Altenberg, L.: The evolution of evolvability in genetic programming. In: Advances in Genetic Programming, K. E. Kinner Jr., ed. MIT Press, (1994) 47–74.Google Scholar
  2. [2]
    Ebner, M., Langguth, P., Albert, J., Shackleton, M. and Shipman, R.: On neutral networks and evolvability. In: Proceedings of the 2001 Congress on Evolutionary Computation,IEEE Press (2001) 1–8.Google Scholar
  3. [3]
    Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge Univ. Press (1983).Google Scholar
  4. [4]
    King, J. L. and Jukes, T. H.: Non-Darwinian evolution. Science Vol. 164 (1969) 788–798.CrossRefGoogle Scholar
  5. [5]
    Koza, J. R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992).Google Scholar
  6. [6]
    Kreitman, M.: The neutral theory is dead. Long live the neutral theory. BioEssays, Vol. 18 (1996) 678–682.CrossRefGoogle Scholar
  7. [7]
    Langdon, W. B. and Poli, R.: Why “building blocks don’t work on parity problems.Technical report number CSRP-98-17. The University of Birmingham, July 13,1998.Google Scholar
  8. [8]
    McDonald, J. H. and Kreitman, M.: Adaptive protein evolution at the Adh locus in Drosophila. Nature Vol. 351 (1991) 652–654.CrossRefGoogle Scholar
  9. [9]
    Miller, J. F. and Thomson, P. Cartesian genetic programming. In: Proceedings of the Third European Conference on Genetic Programming. LNCS, Vol. 1802 (2000) 121–132.Google Scholar
  10. [10]
    Ohta, T.: The nearly neutral theory of molecular evolution. In: Annual Reviews Ecology & Systematic, Vol. 23 (1992) 263–286.CrossRefGoogle Scholar
  11. [11]
    Wilke, C. O., Wang, J. L., Ofria, C., Lenski, R. E. and Adami, C.: Evolution of digital organisms at high mutation rate leads to survival of the flattest. Nature, Vol. 412 (2001) 331–333.CrossRefGoogle Scholar
  12. [12]
    Yu, T. and Miller, J.: Neutrality and the evolvability of Boolean function landscape. In: Proceedings of the Fourth European Conference on Genetic Programming. Springer-Verlag (2001) 204–217.Google Scholar
  13. [13]
    Yu, T.: Structure abstraction and genetic programming. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE Press (1999) 652–659.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Tina Yu
    • 1
  • Julian Miller
    • 2
  1. 1.ChevronTexaco Information Technology CompanySan RamonUSA
  2. 2.School of Computer ScienceUniversity of BirminghamBirminghamUK

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