Performance enhanced genetic programming

  • Chris Clack
  • Tina Yu
Genetic Programming: Issues and Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1213)

Abstract

Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest tasks because of the performance overheads of evolving a large number of data structures, many of which do not correspond to a valid program. We address this problem directly and demonstrate how the evolutionary process can be achieved with much greater efficiency through the use of a formally-based representation and strong typing. We report initial experimental results which demonstrate that our technique exhibits significantly better performance than previous work.

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Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • Chris Clack
    • 1
  • Tina Yu
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonEngland

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