Levy-Flight Genetic Programming: Towards a New Mutation Paradigm

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Christian Darabos and Mario Giacobini and Ting Hu and 
                 Jason H. Moore",
  title =        "Levy-Flight Genetic Programming: Towards a New
                 Mutation Paradigm",
  booktitle =    "10th European Conference on Evolutionary Computation,
                 Machine Learning and Data Mining in Bioinformatics,
                 {EvoBIO 2012}",
  year =         "2012",
  month =        "11-13 " # apr,
  editor =       "Mario Giacobini and Leonardo Vanneschi and 
                 William S. Bush",
  series =       "LNCS",
  volume =       "7246",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  pages =        "38--49",
  organisation = "EvoStar",
  isbn13 =       "978-3-642-29065-7",
  DOI =          "doi:10.1007/978-3-642-29066-4_4",
  size =         "12 pages",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Levy flights are a class of random walks inspired
                 directly by observing animal foraging habits, in which
                 the stride length is drawn from a power-law
                 distribution. This implies that the vast majority of
                 the strides will be short. However, on rare occasions,
                 the stride are gigantic. We use this technique to
                 self-adapt the mutation rate used in Linear Genetic
                 Programming. We apply this original approach to 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 1 over the size of the genotype. We
                 compare different common values of the power-law
                 exponent to the regular spectrum of constant values
                 used habitually. We conclude that our novel method is a
                 viable alternative to constant mutation rate,
                 especially because it tends to reduce the number of
                 parameters of genetic programing.",
  notes =        "Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held
                 in conjunction with EuroGP2012, EvoCOP2012,
                 EvoMusArt2012 and EvoApplications2012",
  affiliation =  "Computational Genetics Laboratory, Dartmouth Medical
                 School, Dartmouth College, Hanover, NH 03755, USA",

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