@inproceedings{daida:2004:vtlodigp,
	title = {Visualizing the Loss of Diversity in Genetic
                 Programming},
	address = {Portland, Oregon},
	author = {Jason M. Daida and David J. Ward and Adam M. Hilss and Stephen L. Long and Mark R. Hodges and Jason T. Kriesel},
	booktitle = {Proceedings of the 2004 IEEE Congress on Evolutionary
                 Computation},
	month = {20-23 June},
	pages = {1225--1232},
	publisher = {IEEE Press},
	url = {http://sitemaker.umich.edu/daida/files/CEC04viz.pdf},
	year = {2004},
	biburl = {http://www.bibsonomy.org/bibtex/29384fb3c02664fe450b326f378c37dd3/brazovayeye},
	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.},
	isbn = {0-7803-8515-2}, notes = {CEC 2004 - A joint meeting of the IEEE, the EPS, and
                 the IEE.},
	keywords = {Computation Evolutionary Foundations Theoretical algorithms, genetic of programming, }
}