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Memory with Memory in Tree-Based Genetic Programming

  • Riccardo Poli
  • Nicholas F. McPhee
  • Luca Citi
  • Ellery Crane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5481)

Abstract

In recent work on linear register-based genetic programming (GP) we introduced the notion of Memory-with-Memory (MwM), where the results of operations are stored in registers using a form of soft assignment which blends a result into the current content of a register rather than entirely replace it. The MwM system yielded very promising results on a set of symbolic regression problems. In this paper, we propose a way of introducing MwM style behaviour in tree-based GP systems. The technique requires only very minor modifications to existing code, and, therefore, is easy to apply. Experiments on a variety of synthetic and real-world problems show that MwM is very beneficial in tree-based GP, too.

Keywords

Genetic Programming Prediction Problem Symbolic Regression Program Size Linear Genetic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Riccardo Poli
    • 1
  • Nicholas F. McPhee
    • 2
  • Luca Citi
    • 1
  • Ellery Crane
    • 2
  1. 1.School of Computer Science and Electronic EngineeringUniversity of EssexUK
  2. 2.Division of Science and MathematicsUniversity of MinnesotaMorrisUSA

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