Fitness Landscape Analysis and Image Filter Evolution
Using Functional-Level CGP
K. Slaný, and L. Sekanina. Proceedings of the 10th European Conference on Genetic
Programming, volume 4445 of Lecture Notes in Computer Science, page 311--320. Valencia, Spain, Springer, (11 - 13 April 2007)
DOI: doi:10.1007/978-3-540-71605-1_29
Abstract
This work analyses fitness landscapes for the image
filter design problem approached using functional-level
Cartesian Genetic Programming. Smoothness and
ruggedness of fitness landscapes are investigated for
five genetic operators. It is shown that the mutation
operator and the single-point crossover operator
generate the smoothest landscapes and thus they are
useful for practical applications in this area. In
contrast to the gate-level evolution, a destructive
behaviour of a simple crossover operator has not been
confirmed.
%0 Conference Paper
%1 eurogp07:Slany
%A Slaný, Karel
%A Sekanina, Lukás
%B Proceedings of the 10th European Conference on Genetic
Programming
%C Valencia, Spain
%D 2007
%E Ebner, Marc
%E O'Neill, Michael
%E Ekárt, Anikó
%E Vanneschi, Leonardo
%E Esparcia-Alcázar, Anna Isabel
%I Springer
%K algorithms, genetic programming
%P 311--320
%R doi:10.1007/978-3-540-71605-1_29
%T Fitness Landscape Analysis and Image Filter Evolution
Using Functional-Level CGP
%V 4445
%X This work analyses fitness landscapes for the image
filter design problem approached using functional-level
Cartesian Genetic Programming. Smoothness and
ruggedness of fitness landscapes are investigated for
five genetic operators. It is shown that the mutation
operator and the single-point crossover operator
generate the smoothest landscapes and thus they are
useful for practical applications in this area. In
contrast to the gate-level evolution, a destructive
behaviour of a simple crossover operator has not been
confirmed.
%@ 3-540-71602-5
@inproceedings{eurogp07:Slany,
abstract = {This work analyses fitness landscapes for the image
filter design problem approached using functional-level
Cartesian Genetic Programming. Smoothness and
ruggedness of fitness landscapes are investigated for
five genetic operators. It is shown that the mutation
operator and the single-point crossover operator
generate the smoothest landscapes and thus they are
useful for practical applications in this area. In
contrast to the gate-level evolution, a destructive
behaviour of a simple crossover operator has not been
confirmed.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Valencia, Spain},
author = {Slan\'y, Karel and Sekanina, Luk\'as},
biburl = {https://www.bibsonomy.org/bibtex/241c5606d59f1b86eefb133c8ebd8cb83/brazovayeye},
booktitle = {Proceedings of the 10th European Conference on Genetic
Programming},
doi = {doi:10.1007/978-3-540-71605-1_29},
editor = {Ebner, Marc and O'Neill, Michael and Ek\'art, Anik\'o and Vanneschi, Leonardo and Esparcia-Alc\'azar, Anna Isabel},
interhash = {52e6a78c34c18fe8c3f78290d703f893},
intrahash = {41c5606d59f1b86eefb133c8ebd8cb83},
isbn = {3-540-71602-5},
isbn13 = {978-3-540-71602-0},
keywords = {algorithms, genetic programming},
month = {11 - 13 April},
notes = {Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007},
pages = {311--320},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2008-06-19T17:51:47.000+0200},
title = {Fitness Landscape Analysis and Image Filter Evolution
Using Functional-Level {CGP}},
volume = 4445,
year = 2007
}