Ordinal Regression in Evolutionary Computation

  • Thomas Philip Runarsson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


Surrogate ranking in evolutionary computation using ordinal regression is introduced. The fitness of individual points is indirectly estimated by modeling their rank. The aim is to reduce the number of costly fitness evaluations needed for evolution. The ordinal regression, or preference learning, implements a kernel-defined feature space and an optimization technique by which the margin between rank boundaries is maximized. The technique is illustrated on some classical numerical optimization functions using an evolution strategy. The benefits of surrogate ranking, compared to surrogates that model the fitness function directly, are discussed.


Evolutionary Computation Polynomial Kernel Ordinal Regression Search Point Training Point 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thomas Philip Runarsson
    • 1
  1. 1.Science InstituteUniversity of Iceland 

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