Visualizing the Loss of Diversity in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{daida:2004:vtlodigp,

• title = "Visualizing the Loss of Diversity in Genetic Programming",
• author = "Jason M. Daida and David J. Ward and Adam M. Hilss and Stephen L. Long and Mark R. Hodges and Jason T. Kriesel",
• pages = "1225--1232",
• booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary Computation",
• year = "2004",
• publisher = "IEEE Press",
• month = "20-23 " # jun,
• address = "Portland, Oregon",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Theoretical Foundations of Evolutionary Computation",
• URL = "http://sitemaker.umich.edu/daida/files/CEC04viz.pdf",
• DOI = "doi:10.1109/CEC.2004.1331037",
• 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.",
• notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and the IEE.",

}

Genetic Programming entries for Jason M Daida David J Ward Adam M Hilss Stephen L Long Mark Hodges Jason T Kriesel

Citations