Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{hong:2013:EuroGP,
  author =       "Libin Hong and John Woodward and Jingpeng Li and 
                 Ender Ozcan",
  title =        "Automated Design of Probability Distributions as
                 Mutation Operators for Evolutionary Programming Using
                 Genetic Programming",
  booktitle =    "Proceedings of the 16th European Conference on Genetic
                 Programming, EuroGP 2013",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and 
                 A. Sima Uyar and Bin Hu",
  series =       "LNCS",
  volume =       "7831",
  publisher =    "Springer Verlag",
  address =      "Vienna, Austria",
  pages =        "85--96",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Programming, Function Optimisation, Machine Learning,
                 Meta-learning, Hyper-heuristics, Automatic Design.",
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_8",
  abstract =     "The mutation operator is the only source of variation
                 in Evolutionary Programming. In the past these have
                 been human nominated and included the Gaussian, Cauchy,
                 and the Levy distributions. We automatically design
                 mutation operators (probability distributions) using
                 Genetic Programming. This is done by using a standard
                 Gaussian random number generator as the terminal set
                 and basic arithmetic operators as the function set. In
                 other words, an arbitrary random number generator is a
                 function of a randomly (Gaussian) generated number
                 passed through an arbitrary function generated by
                 Genetic Programming.

                 Rather than engaging in the futile attempt to develop
                 mutation operators for arbitrary benchmark functions
                 (which is a consequence of the No Free Lunch theorems),
                 we consider tailoring mutation operators for particular
                 function classes. We draw functions from a function
                 class (a probability distribution over a set of
                 functions). The mutation probability distribution is
                 trained on a set of function instances drawn from a
                 given function class. It is then tested on a separate
                 independent test set of function instances to confirm
                 that the evolved probability distribution has indeed
                 generalized to the function class.

                 Initial results are highly encouraging: on each of the
                 ten function classes the probability distributions
                 generated using Genetic Programming outperform both the
                 Gaussian and Cauchy distributions.",
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",
}

Genetic Programming entries for Libin Hong John R Woodward Jingpeng Li Ender Ozcan

Citations