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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)

Abstract

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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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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