- author = "Nicholas Freitag McPhee and Nicholas J. Hopper",
- title = "{AppGP}: An Alternative Structural Representation for {GP}",
- 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 = "2",
- pages = "1377--1383",
- 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, models of evolutionary computation, AppGP, local maxima, performance, standard genetic programming, standard subtree crossover, structural convergence",
- ISBN = "0-7803-5536-9 (softbound)",
- ISBN = "0-7803-5537-7 (Microfiche)",
-
URL = "
https://www-users.cs.umn.edu/~hoppernj/appgp.ps",
-
DOI = "
10.1109/CEC.1999.782643",
- size = "7 pages",
- abstract = "It has been shown that standard genetic programming using standard subtree crossover is prone to a form of structural convergence which makes it extremely difficult to make changes near the root, occasionally causing runs to become trapped in local maxima. Based on these structural limitations we propose a different tree representation, AppGP, which we hope will avoid this problem in some cases. In this paper, we describe this representation, and compare its performance to the performance of standard GP on a suite of test problems. We find that on all of the test problems, AppGP does no worse than standard GP, and in several it does considerably better, suggesting that the representation warrants further study",
-
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
