Using Loops in Genetic Programming for a Two Class
Binary Image Classification Problem
X. Li, и V. Ciesielski. AI 2004: Advances in Artificial Intelligence:
Proceedings of the 17th Australian Joint Conference on
Artificial Intelligence, том 3339 из Lecture Notes in Computer Science, стр. 898--909. Cairns, Australia, Springer, (декабря 2004)
DOI: doi:10.1007/b104336
Аннотация
Loops are rarely used in genetic programming (GP),
because they lead to massive computation due to the
increase in the size of the search space. We have
investigated the use of loops with restricted semantics
for a problem in which there are natural repetitive
elements, that of distinguishing two classes of images.
Using our formulation, programs with loops were
successfully evolved and performed much better than
programs without loops. Our results suggest that loops
can successfully used in genetic programming in
situations where domain knowledge is available to
provide some restrictions on loop semantics.
%0 Conference Paper
%1 LiCie04
%A Li, Xiang
%A Ciesielski, Vic
%B AI 2004: Advances in Artificial Intelligence:
Proceedings of the 17th Australian Joint Conference on
Artificial Intelligence
%C Cairns, Australia
%D 2004
%E Webb, Geoffrey I.
%E Yu, Xinghuo
%I Springer
%K algorithms, classification classification, genetic image problem programming,
%P 898--909
%R doi:10.1007/b104336
%T Using Loops in Genetic Programming for a Two Class
Binary Image Classification Problem
%U http://www.springerlink.com/index/6MDEKV7A1821E0UY
%V 3339
%X Loops are rarely used in genetic programming (GP),
because they lead to massive computation due to the
increase in the size of the search space. We have
investigated the use of loops with restricted semantics
for a problem in which there are natural repetitive
elements, that of distinguishing two classes of images.
Using our formulation, programs with loops were
successfully evolved and performed much better than
programs without loops. Our results suggest that loops
can successfully used in genetic programming in
situations where domain knowledge is available to
provide some restrictions on loop semantics.
%@ 3-540-24059-4
@inproceedings{LiCie04,
abstract = {Loops are rarely used in genetic programming (GP),
because they lead to massive computation due to the
increase in the size of the search space. We have
investigated the use of loops with restricted semantics
for a problem in which there are natural repetitive
elements, that of distinguishing two classes of images.
Using our formulation, programs with loops were
successfully evolved and performed much better than
programs without loops. Our results suggest that loops
can successfully used in genetic programming in
situations where domain knowledge is available to
provide some restrictions on loop semantics.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Cairns, Australia},
author = {Li, Xiang and Ciesielski, Vic},
biburl = {https://www.bibsonomy.org/bibtex/29913c508bc0ff48ee2ac674c53b44e55/brazovayeye},
booktitle = {AI 2004: Advances in Artificial Intelligence:
Proceedings of the 17th Australian Joint Conference on
Artificial Intelligence},
doi = {doi:10.1007/b104336},
editor = {Webb, Geoffrey I. and Yu, Xinghuo},
interhash = {aac25a83ad82c0050b1347bf5191da5c},
intrahash = {9913c508bc0ff48ee2ac674c53b44e55},
isbn = {3-540-24059-4},
keywords = {algorithms, classification classification, genetic image problem programming,},
month = {December 4-6},
pages = {898--909},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2008-06-19T17:45:33.000+0200},
title = {Using Loops in Genetic Programming for a Two Class
Binary Image Classification Problem},
url = {http://www.springerlink.com/index/6MDEKV7A1821E0UY},
volume = 3339,
year = 2004
}