@inproceedings{bea81f520d684ceeae7d7e9f55d81ba6,

title = "Automated design of probability distributions as mutation operators for evolutionary programming using genetic programming",

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 L{\'e}vy 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 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.",

keywords = "Automatic Design, Evolutionary Programming, Function Optimization, Genetic Programming, Hyper-heuristics, Machine Learning, Meta-learning",

author = "Libin Hong and Woodward, {John R.} and Jingpeng Li and Ender {\"O}zcan",

year = "2013",

doi = "10.1007/978-3-642-37207-0_8",

language = "English",

isbn = "9783642372063",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "85--96",

booktitle = "Genetic Programming - 16th European Conference, EuroGP 2013, Proceedings",

address = "Germany",

note = "16th European Conference on Genetic Programming, EuroGP 2013 ; Conference date: 03-04-2013 Through 05-04-2013",

}