Universal Approximation by Genetic Programming

author = "Xin Yao",

title = "Universal Approximation by Genetic Programming",

booktitle = "Foundations of Genetic Programming",

year = "1999",

editor = "Thomas Haynes and William B. Langdon and Una-May O'Reilly and Riccardo Poli and Justinian Rosca",

pages = "66--67",

address = "Orlando, Florida, USA",

month = "13 " # jul,

keywords = "genetic algorithms, genetic programming",

URL = "

http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/yao.ps.gz",

size = "2 pages",

abstract = "Genetic programming GP has been applied successfully to many difficult problems. However, little theory is currently available to explain why GP works or does not work for a particular problem. We investigate the power of GP in terms of its approximation capability to arbitrary functions. The relationship between artificial neural networks ANNs and GP is discussed. Such relation ship enables us to apply the existing theoretical results in ANNs to GP and show that GP can be a universal approximator. This result shows at least partially why GP is capable of solving some very difficult problems. It also sheds some light on choosing the function set for GP applications.",

notes = "GECCO'99 WKSHOP, part of \cite{haynes:1999:fogp}",