R. Abbott, B. Parviz, and C. Sun. Proceedings of the International Conference on
Artificial Intelligence, IC-AI '04, Volume 2 &
Proceedings of the International Conference on Machine
Learning; Models, Technologies & Applications, MLMTA
'04, 2, page 1113--1116. Las Vegas, Nevada, USA, CSREA Press, (June 2004)
Abstract
Even though the Genetic Programming (GP) mechanism is
capable of evolving any computable function, the means
through which it does so is inherently flawed: the user
must provide the GP engine with an evolutionary pathway
toward a solution. Hence Genetic Programming is
problematic as a mechanism for generating creative
solutions to specific problems.
Proceedings of the International Conference on
Artificial Intelligence, IC-AI '04, Volume 2 &
Proceedings of the International Conference on Machine
Learning; Models, Technologies & Applications, MLMTA
'04
%0 Conference Paper
%1 DBLP:conf/icai/AbbottPS04
%A Abbott, Russ
%A Parviz, Behzad
%A Sun, Chengyu
%B Proceedings of the International Conference on
Artificial Intelligence, IC-AI '04, Volume 2 &
Proceedings of the International Conference on Machine
Learning; Models, Technologies & Applications, MLMTA
'04
%C Las Vegas, Nevada, USA
%D 2004
%E Arabnia, Hamid R.
%E Mun, Youngsong
%I CSREA Press
%K adaptive algorithms, evolution evolution, evolutionary fitness function, genetic pathway, programming, teleological
%P 1113--1116
%T Genetic Programming Reconsidered
%U http://abbott.calstatela.edu/PapersAndTalks/GeneticProgrammingReconsidered.pdf
%V 2
%X Even though the Genetic Programming (GP) mechanism is
capable of evolving any computable function, the means
through which it does so is inherently flawed: the user
must provide the GP engine with an evolutionary pathway
toward a solution. Hence Genetic Programming is
problematic as a mechanism for generating creative
solutions to specific problems.
%@ 1-932415-32-7
@inproceedings{DBLP:conf/icai/AbbottPS04,
abstract = {Even though the Genetic Programming (GP) mechanism is
capable of evolving any computable function, the means
through which it does so is inherently flawed: the user
must provide the GP engine with an evolutionary pathway
toward a solution. Hence Genetic Programming is
problematic as a mechanism for generating creative
solutions to specific problems.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Las Vegas, Nevada, USA},
author = {Abbott, Russ and Parviz, Behzad and Sun, Chengyu},
bibsource = {DBLP, http://dblp.uni-trier.de},
biburl = {https://www.bibsonomy.org/bibtex/2cdb3ba2b75caf95c47355083b81abda2/brazovayeye},
booktitle = {Proceedings of the International Conference on
Artificial Intelligence, IC-AI '04, Volume 2 {\&}
Proceedings of the International Conference on Machine
Learning; Models, Technologies {\&} Applications, MLMTA
'04},
editor = {Arabnia, Hamid R. and Mun, Youngsong},
interhash = {f3f702b0f76fd536d5cbadd35660cf0b},
intrahash = {cdb3ba2b75caf95c47355083b81abda2},
isbn = {1-932415-32-7},
keywords = {adaptive algorithms, evolution evolution, evolutionary fitness function, genetic pathway, programming, teleological},
month = {June 21-24},
notes = {sort, fitness function cheat},
pages = {1113--1116},
publisher = {CSREA Press},
size = {4 pages},
timestamp = {2008-06-19T17:35:07.000+0200},
title = {Genetic Programming Reconsidered},
url = {http://abbott.calstatela.edu/PapersAndTalks/GeneticProgrammingReconsidered.pdf},
volume = 2,
year = 2004
}