- author = "Janine H. Imada and Brian J. Ross",
- title = "Using feature-based fitness evaluation in symbolic regression with added noise",
- year = "2008",
- editor = "Marc Ebner and Mike Cattolico and Jano {van Hemert} and Steven Gustafson and Laurence D. Merkle and Frank W. Moore and Clare Bates Congdon and Christopher D. Clack and Frank W. Moore and William Rand and Sevan G. Ficici and Rick Riolo and Jaume Bacardit and Ester Bernado-Mansilla and Martin V. Butz and Stephen L. Smith and Stefano Cagnoni and Mark Hauschild and Martin Pelikan and Kumara Sastry",
- isbn13 = "978-1-60558-131-6",
- booktitle = "GECCO-2008 Late-Breaking Papers",
- pages = "2153--2158",
- address = "Atlanta, GA, USA",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p2153.pdf",
-
DOI = "
doi:10.1145/1388969.1389039",
- organisation = "SIGEvo",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "12-16 " # jul,
- keywords = "genetic algorithms, genetic programming, noisy signals, symbolic regression",
- size = "5 pages",
- abstract = "Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the dependent variable directly calculated from the candidate expression. While this approach is extremely successful in many instances, its performance can deteriorate in the presence of noise. In this paper, a feature-based fitness function is considered, in which the fitness scores are determined by comparing the statistical features of the sequence of values, rather than the actual values themselves. The set of features used in the fitness evaluation are customized according to the target, and are drawn from a wide set of features capable of characterizing a variety of behaviours. Experiments examining the performance of the feature-based and standard fitness functions are carried out for non-oscillating and oscillating targets in a GP system which introduces noise during the evaluation of candidate expressions. Results show strength in the feature-based fitness function, especially for the oscillating target.",
-
notes = "Distributed on CD-ROM at GECCO-2008
ACM Order Number 910081. Also known as \cite{1389039}",
