We define a set of measures that capture some
different aspects of neutrality in evolutionary
algorithms fitness landscapes from a qualitative point
of view. If considered all together, these measures
offer a rather complete picture of the characteristics
of fitness landscapes bound to neutrality and may be
used as broad indicators of problem hardness. We
compare the results returned by these measures with the
ones of negative slope coefficient, a quantitative
measure of problem hardness that has been recently
defined and with success rate statistics on a well
known genetic programming benchmark: the multiplexer
problem. In order to efficaciously study the search
space, we use a sampling technique that has recently
been introduced and we show its suitability on this
problem.
%0 Conference Paper
%1 eurogp07:vanneschi
%A Vanneschi, Leonardo
%A Tomassini, Marco
%A Collard, Philippe
%A Verel, Sébastien
%A Pirola, Yuri
%A Mauri, Giancarlo
%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 241--250
%R doi:10.1007/978-3-540-71605-1_22
%T A Comprehensive View of Fitness Landscapes with
Neutrality and Fitness Clouds
%V 4445
%X We define a set of measures that capture some
different aspects of neutrality in evolutionary
algorithms fitness landscapes from a qualitative point
of view. If considered all together, these measures
offer a rather complete picture of the characteristics
of fitness landscapes bound to neutrality and may be
used as broad indicators of problem hardness. We
compare the results returned by these measures with the
ones of negative slope coefficient, a quantitative
measure of problem hardness that has been recently
defined and with success rate statistics on a well
known genetic programming benchmark: the multiplexer
problem. In order to efficaciously study the search
space, we use a sampling technique that has recently
been introduced and we show its suitability on this
problem.
%@ 3-540-71602-5
@inproceedings{eurogp07:vanneschi,
abstract = {We define a set of measures that capture some
different aspects of neutrality in evolutionary
algorithms fitness landscapes from a qualitative point
of view. If considered all together, these measures
offer a rather complete picture of the characteristics
of fitness landscapes bound to neutrality and may be
used as broad indicators of problem hardness. We
compare the results returned by these measures with the
ones of negative slope coefficient, a quantitative
measure of problem hardness that has been recently
defined and with success rate statistics on a well
known genetic programming benchmark: the multiplexer
problem. In order to efficaciously study the search
space, we use a sampling technique that has recently
been introduced and we show its suitability on this
problem.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {Valencia, Spain},
author = {Vanneschi, Leonardo and Tomassini, Marco and Collard, Philippe and Verel, S\'ebastien and Pirola, Yuri and Mauri, Giancarlo},
biburl = {https://www.bibsonomy.org/bibtex/2c40f6ed6b3646a2cfce60975c0175b42/brazovayeye},
booktitle = {Proceedings of the 10th European Conference on Genetic
Programming},
doi = {doi:10.1007/978-3-540-71605-1_22},
editor = {Ebner, Marc and O'Neill, Michael and Ek\'art, Anik\'o and Vanneschi, Leonardo and Esparcia-Alc\'azar, Anna Isabel},
interhash = {0517fc1bd7ac2d309aed46c6abf6d86d},
intrahash = {c40f6ed6b3646a2cfce60975c0175b42},
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 = {241--250},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2008-06-19T17:53:35.000+0200},
title = {A Comprehensive View of Fitness Landscapes with
Neutrality and Fitness Clouds},
volume = 4445,
year = 2007
}