A New Mutation Paradigm for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Christian Darabos and Mario Giacobini and Ting Hu and 
                 Jason H. Moore",
  title =        "A New Mutation Paradigm for Genetic Programming",
  booktitle =    "Genetic Programming Theory and Practice X",
  year =         "2012",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Marylyn D. Ritchie and Jason H. Moore",
  publisher =    "Springer",
  chapter =      "4",
  pages =        "45--58",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 computation, Levy-flight, Random walks",
  isbn13 =       "978-1-4614-6845-5",
  URL =          "http://dx.doi.org/10.1007/978-1-4614-6846-2_4",
  DOI =          "doi:10.1007/978-1-4614-6846-2_4",
  abstract =     "Levy flights are a class of random walks directly
                 inspired by observing animal foraging habits, where a
                 power-law distribution of the stride length can be
                 often observed. This implies that, while the vast
                 majority of the strides will be short, on rare
                 occasions, the strides are gigantic. We propose a
                 mutation mechanism in Linear Genetic Programming
                 inspired by this ethological behaviour, thus obtaining
                 a self-adaptive mutation rate. We experimentally test
                 this original approach on three different classes of
                 problems: Boolean regression, quadratic polynomial
                 regression, and surface reconstruction. We find that in
                 all cases, our method outperforms the generic, commonly
                 used constant mutation rate of one over the size of the
                 genotype. Moreover, we compare different common values
                 of the power-law exponent to the another self-adaptive
                 mutation mechanism directly inspired by Simulated
                 Annealing. We conclude that our novel method is a
                 viable alternative to constant and self-adaptive
                 mutation rates, especially because it tends to reduce
                 the number of parameters of genetic programming.",
  notes =        "part of \cite{Riolo:2012:GPTP} published after the
                 workshop in 2013",

Genetic Programming entries for Christian Darabos Mario Giacobini Ting Hu Jason H Moore