S. Kahrs. Proceedings of the 8th annual Conference on Genetic and Evolutionary Computation (GECCO'06), page 941--942. New York, NY, USA, ACM, (2006)
Abstract
When Genetic Programming is used to evolve arithmetic functions it often operates by composing them from a fixed collection of elementary operators and applying them to parameters or certain primitive constants. This limits the expressiveness of the programs that can be evolved. It is possible to extend the expressiveness of such an approach significantly without leaving the comfort of terminating programs by including primitive recursion as a control operation.The technique used here was gene expression programming 2, a variation of grammatical evolution 8. Grammatical evolution avoids the problem of program bloat; its separation of genotype (string of symbols) and phenotype (expression tree) permits to optimise the generated programs without interfering with the evolutionary process.
%0 Conference Paper
%1 Kahrs06
%A Kahrs, Stefan
%B Proceedings of the 8th annual Conference on Genetic and Evolutionary Computation (GECCO'06)
%C New York, NY, USA
%D 2006
%I ACM
%K enumerative_ip gp ifp induction inductive_programming primitive_recursion program_evolution program_synthesis
%P 941--942
%T Genetic Programming with Primitive Recursion
%U http://doi.acm.org/10.1145/1143997.1144160
%X When Genetic Programming is used to evolve arithmetic functions it often operates by composing them from a fixed collection of elementary operators and applying them to parameters or certain primitive constants. This limits the expressiveness of the programs that can be evolved. It is possible to extend the expressiveness of such an approach significantly without leaving the comfort of terminating programs by including primitive recursion as a control operation.The technique used here was gene expression programming 2, a variation of grammatical evolution 8. Grammatical evolution avoids the problem of program bloat; its separation of genotype (string of symbols) and phenotype (expression tree) permits to optimise the generated programs without interfering with the evolutionary process.
@inproceedings{Kahrs06,
abstract = {When Genetic Programming is used to evolve arithmetic functions it often operates by composing them from a fixed collection of elementary operators and applying them to parameters or certain primitive constants. This limits the expressiveness of the programs that can be evolved. It is possible to extend the expressiveness of such an approach significantly without leaving the comfort of terminating programs by including primitive recursion as a control operation.The technique used here was gene expression programming [2], a variation of grammatical evolution [8]. Grammatical evolution avoids the problem of program bloat; its separation of genotype (string of symbols) and phenotype (expression tree) permits to optimise the generated programs without interfering with the evolutionary process.},
added-at = {2008-11-21T20:22:56.000+0100},
address = {New York, NY, USA},
author = {Kahrs, Stefan},
biburl = {https://www.bibsonomy.org/bibtex/2906ac191efc0601092391474cc3b3a68/emanuel},
booktitle = {Proceedings of the 8th annual Conference on Genetic and Evolutionary Computation ({GECCO'06})},
interhash = {89840d21ebf90f5c242c4c59cc68836b},
intrahash = {906ac191efc0601092391474cc3b3a68},
keywords = {enumerative_ip gp ifp induction inductive_programming primitive_recursion program_evolution program_synthesis},
pages = {941--942},
publisher = {ACM},
timestamp = {2008-11-21T20:22:56.000+0100},
title = {Genetic Programming with Primitive Recursion},
url = {http://doi.acm.org/10.1145/1143997.1144160},
year = 2006
}