- 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",
