Visualizing the Loss of Diversity in Genetic
Programming
J. Daida, D. Ward, A. Hilss, S. Long, M. Hodges, and J. Kriesel. Proceedings of the 2004 IEEE Congress on Evolutionary
Computation, page 1225--1232. Portland, Oregon, IEEE Press, (20-23 June 2004)
Abstract
This paper introduces visualization techniques that
allow for a multivariate approach in understanding the
dynamics that underlie genetic programming (GP).
Emphasis is given toward understanding the relationship
between problem difficulty and the loss of diversity.
The visualizations raise questions about diversity and
problem solving efficacy, as well as the role of the
initial population in determining solution outcomes.
%0 Conference Paper
%1 daida:2004:vtlodigp
%A Daida, Jason M.
%A Ward, David J.
%A Hilss, Adam M.
%A Long, Stephen L.
%A Hodges, Mark R.
%A Kriesel, Jason T.
%B Proceedings of the 2004 IEEE Congress on Evolutionary
Computation
%C Portland, Oregon
%D 2004
%I IEEE Press
%K Computation Evolutionary Foundations Theoretical algorithms, genetic of programming,
%P 1225--1232
%T Visualizing the Loss of Diversity in Genetic
Programming
%U http://sitemaker.umich.edu/daida/files/CEC04viz.pdf
%X This paper introduces visualization techniques that
allow for a multivariate approach in understanding the
dynamics that underlie genetic programming (GP).
Emphasis is given toward understanding the relationship
between problem difficulty and the loss of diversity.
The visualizations raise questions about diversity and
problem solving efficacy, as well as the role of the
initial population in determining solution outcomes.
%@ 0-7803-8515-2
@inproceedings{daida:2004:vtlodigp,
abstract = {This paper introduces visualization techniques that
allow for a multivariate approach in understanding the
dynamics that underlie genetic programming (GP).
Emphasis is given toward understanding the relationship
between problem difficulty and the loss of diversity.
The visualizations raise questions about diversity and
problem solving efficacy, as well as the role of the
initial population in determining solution outcomes.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Portland, Oregon},
author = {Daida, Jason M. and Ward, David J. and Hilss, Adam M. and Long, Stephen L. and Hodges, Mark R. and Kriesel, Jason T.},
biburl = {https://www.bibsonomy.org/bibtex/29384fb3c02664fe450b326f378c37dd3/brazovayeye},
booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary
Computation},
interhash = {ac3a182f69597517f55cbe82393e0849},
intrahash = {9384fb3c02664fe450b326f378c37dd3},
isbn = {0-7803-8515-2},
keywords = {Computation Evolutionary Foundations Theoretical algorithms, genetic of programming,},
month = {20-23 June},
notes = {CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.},
pages = {1225--1232},
publisher = {IEEE Press},
timestamp = {2008-06-19T17:38:22.000+0200},
title = {Visualizing the Loss of Diversity in Genetic
Programming},
url = {http://sitemaker.umich.edu/daida/files/CEC04viz.pdf},
year = 2004
}