Matrix Analysis of Genetic Programming Mutation

  • Andrew J. Parkes
  • Ender Özcan
  • Matthew R. Hyde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7244)


Heuristic policies for combinatorial optimisation problems can be found by using Genetic programming (GP) to evolve a mathematical function over variables given by the current state of the problem, and whose value is used to determine action choices (such as preferred assignments or branches). If all variables have finite discrete domains, then the expressions can be converted to an equivalent lookup table or ‘decision matrix’. Spaces of such matrices often have natural distance metrics (after conversion to a standard form). As a case study, and to support the understanding of GP as a meta-heuristic, we extend previous bin-packing work and compare the distances between matrices from before and after a GP-driven mutation. We find that GP mutations often correspond to large moves within the space of decision matrices. This strengthens evidence that the role of mutations within GP might be somewhat different than their role within Genetic Algorithms.


Genetic programming Genotype-phenotype mapping 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrew J. Parkes
    • 1
  • Ender Özcan
    • 1
  • Matthew R. Hyde
    • 1
  1. 1.School of Computer ScienceThe University of NottinghamNottinghamUnited Kingdom (UK)

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