Adaptive Genetic Programming for Dynamic Classification Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Riekert:2009:cec,

• author = "M. Riekert and K. M. Malan and A. P. Engelbrecht",
• title = "Adaptive Genetic Programming for Dynamic Classification Problems",
• booktitle = "2009 IEEE Congress on Evolutionary Computation",
• year = "2009",
• editor = "Andy Tyrrell",
• pages = "674--681",
• address = "Trondheim, Norway",
• month = "18-21 " # may,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming, AGP, Gradient Descent GD",
• isbn13 = "978-1-4244-2959-2",
• file = "P327.pdf",
• DOI = "doi:10.1109/CEC.2009.4983010",
• size = "8 pages",
• abstract = "This paper investigates the feasibility of using Genetic Programming in dynamically changing environments to evolve decision trees for classification problems and proposes an new version of Genetic Programming called Adaptive Genetic Programming. It does so by comparing the performance or classification error of Genetic Programming and Adaptive Genetic Programming to that of Gradient Descent in abruptly and progressively changing environments. To cope with dynamic environments, Adaptive Genetic Programming incorporates adaptive control parameters, variable elitism and culling. Results show that both Genetic Programming and Adaptive Genetic Programming are viable algorithms for dynamic environments yielding a performance gain over Gradient Descent for lower dimensional problems even with severe environment changes. In addition, Adaptive Genetic Programming performs slightly better than Genetic Programming, due to faster recovery from changes in the environment.",
• notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR",

}

Genetic Programming entries for Marius Riekert Katherine M Malan Andries P Engelbrecht

Citations