Using Fitness Distributions to Improve the Evolution of Learning Structures

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{igel:1999:UFDIELS,

• author = "Christian Igel and Martin Kreutz",
• title = "Using Fitness Distributions to Improve the Evolution of Learning Structures",
• booktitle = "Proceedings of the Congress on Evolutionary Computation",
• year = "1999",
• editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala",
• volume = "3",
• pages = "1902--1909",
• address = "Mayflower Hotel, Washington D.C., USA",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "6-9 " # jul,
• organisation = "Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, fitness distributions, density estimation, gradient-based operators, absolute benefit, coefficient adaptation, density estimation models, fitness distributions, fitness space, fitness trajectory analysis, gradient based operators, gradient information, information theory based measure, learning structure evolution, offline analysis, online operator adaptation, information theory, learning (artificial intelligence), probability",
• ISBN = "0-7803-5536-9 (softbound)",
• ISBN = "0-7803-5537-7 (Microfiche)",
• URL = "http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/igel/UFDtItEoLS.ps.gz",
• URL = "http://citeseer.ist.psu.edu/294668.html",
• DOI = "10.1109/CEC.1999.785505",
• abstract = "the absolute benefit, a measure of improvement in the fitness space, is derived from the viewpoint of fitness distribution and fitness trajectory analysis. It is used for online operator-adaptation, where the optimisation of density estimation models serves as an example. A new information theory based measure is proposed to judge the accuracy of the evolved models. Further, the absolute benefit is applied to offline analysis of new gradient based operators used for coefficient adaptation in genetic programming. An efficient method to calculate the gradient information is presented.",
• notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE.

  Library of Congress Number = 99-61143",

}

Genetic Programming entries for Christian Igel Martin Kreutz

Citations