%0 Conference Paper %1 igel:1999:UFDIELS %A Igel, Christian %A Kreutz, Martin %B Proceedings of the Congress on Evolutionary Computation %C Mayflower Hotel, Washington D.C., USA %D 1999 %E Angeline, Peter J. %E Michalewicz, Zbyszek %E Schoenauer, Marc %E Yao, Xin %E Zalzala, Ali %I IEEE Press %K algorithms, density distributions, estimation, fitness genetic gradient-based operators programming, %P 1902--1909 %T Using Fitness Distributions to Improve the Evolution of Learning Structures %U http://citeseer.ist.psu.edu/294668.html %V 3 %X 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. %@ 0-7803-5537-7 (Microfiche)

@inproceedings{igel:1999:UFDIELS, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Mayflower Hotel, Washington D.C., USA}, author = {Igel, Christian and Kreutz, Martin}, biburl = {https://www.bibsonomy.org/bibtex/2cb75c5a1c05cce18dc187c31efb994b1/brazovayeye}, booktitle = {Proceedings of the Congress on Evolutionary Computation}, editor = {Angeline, Peter J. and Michalewicz, Zbyszek and Schoenauer, Marc and Yao, Xin and Zalzala, Ali}, interhash = {c72b346f47d39bee0fb9389647ddcfe0}, intrahash = {cb75c5a1c05cce18dc187c31efb994b1}, isbn = {0-7803-5537-7 (Microfiche)}, keywords = {algorithms, density distributions, estimation, fitness genetic gradient-based operators programming,}, month = {6-9 July}, notes = {CEC-99 - A joint meeting of the IEEE, Evolutionary Programming Society, Galesia, and the IEE. Library of Congress Number = 99-61143}, organisation = {Congress on Evolutionary Computation, IEEE / Neural Networks Council, Evolutionary Programming Society, Galesia, IEE}, pages = {1902--1909}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, timestamp = {2008-06-19T17:42:06.000+0200}, title = {Using Fitness Distributions to Improve the Evolution of Learning Structures}, url = {http://citeseer.ist.psu.edu/294668.html}, volume = 3, year = 1999 }