Learning Recursive Functions with Object Oriented
Genetic Programming
A. Agapitos, и S. Lucas. Proceedings of the 9th European Conference on Genetic
Programming, том 3905 из Lecture Notes in Computer Science, стр. 166--177. Budapest, Hungary, Springer, (10 - 12 April 2006)
Аннотация
This paper describes the evolution of recursive
functions within an Object-Oriented Genetic Programming
(OOGP) system. We evolved general solutions to
factorial, Fibonacci, exponentiation, even-n-Parity,
and nth-3. We report the computational effort required
to evolve these methods and provide a comparison
between crossover and mutation variation operators, and
also undirected random search. We found that the
evolutionary algorithms performed much better than
undirected random search, and that mutation
outperformed crossover on most problems.
%0 Conference Paper
%1 eurogp06:AgapitosLucas
%A Agapitos, Alexandros
%A Lucas, Simon M.
%B Proceedings of the 9th European Conference on Genetic
Programming
%C Budapest, Hungary
%D 2006
%E Collet, Pierre
%E Tomassini, Marco
%E Ebner, Marc
%E Gustafson, Steven
%E Ekárt, Anikó
%I Springer
%K algorithms, genetic programming
%P 166--177
%T Learning Recursive Functions with Object Oriented
Genetic Programming
%U http://link.springer.de/link/service/series/0558/papers/3905/39050166.pdf
%V 3905
%X This paper describes the evolution of recursive
functions within an Object-Oriented Genetic Programming
(OOGP) system. We evolved general solutions to
factorial, Fibonacci, exponentiation, even-n-Parity,
and nth-3. We report the computational effort required
to evolve these methods and provide a comparison
between crossover and mutation variation operators, and
also undirected random search. We found that the
evolutionary algorithms performed much better than
undirected random search, and that mutation
outperformed crossover on most problems.
%@ 3-540-33143-3
@inproceedings{eurogp06:AgapitosLucas,
abstract = {This paper describes the evolution of recursive
functions within an Object-Oriented Genetic Programming
(OOGP) system. We evolved general solutions to
factorial, Fibonacci, exponentiation, even-n-Parity,
and nth-3. We report the computational effort required
to evolve these methods and provide a comparison
between crossover and mutation variation operators, and
also undirected random search. We found that the
evolutionary algorithms performed much better than
undirected random search, and that mutation
outperformed crossover on most problems.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Budapest, Hungary},
author = {Agapitos, Alexandros and Lucas, Simon M.},
bibsource = {DBLP, http://dblp.uni-trier.de},
biburl = {https://www.bibsonomy.org/bibtex/2edc8b24a983eeef9a02e0b5b90e85b30/brazovayeye},
booktitle = {Proceedings of the 9th European Conference on Genetic
Programming},
editor = {Collet, Pierre and Tomassini, Marco and Ebner, Marc and Gustafson, Steven and Ek\'art, Anik\'o},
interhash = {2d4e87c92f9a36cda8734e04ea2beee4},
intrahash = {edc8b24a983eeef9a02e0b5b90e85b30},
isbn = {3-540-33143-3},
keywords = {algorithms, genetic programming},
month = {10 - 12 April},
notes = {Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006
Java reflection.},
organisation = {EvoNet},
pages = {166--177},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2008-06-19T17:35:14.000+0200},
title = {Learning Recursive Functions with Object Oriented
Genetic Programming},
url = {http://link.springer.de/link/service/series/0558/papers/3905/39050166.pdf},
volume = 3905,
year = 2006
}