- author = "E. Howard N. Oakley",
- title = "The application of genetic programming to the investigation of short, noisy, chaotic data series",
- booktitle = "Evolutionary Computing, AISB workshop",
- publisher = "Springer-Verlag",
- year = "1994",
- editor = "T. C. Fogarty",
- volume = "865",
- series = "Lecture Notes in Computer Science",
- pages = "320--332",
- address = "Leeds, UK",
- month = "11-13 " # apr,
- organisation = "AISB",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "3-540-58483-8",
- broken = "http://www.io.com/~ftp/genetic-programming/papers/OAKPAPRS.TARR.GZ",
-
DOI = "
DOI:10.1007/3-540-58483-8_24",
- size = "10 pages",
-
abstract = "Techniques to investigate chaotic data require long
noise-free series. Genetic programming allows fitting
of arbitrary functions to short noisy datasets.
Conventional genetic programming was used to fit Lisp
S-expressions to a known chaotic series (the
Mackey-Glass equation, discretized to a map) with added
noise. Embedding was performed by including previous
values in time in the terminal set. Prediction
intervals were 20--1065 steps into the future, based
upon near-minimal 35 training points from the
series.
Fittest S-expressions yielded useful structural information. Semilogarithmic plots of normalised root mean squared error of the fittest forecasts against the length of forecast showed two dominant slopes. Noise led to a small exponential increase in this error. Genetic programming appears useful, as it compares favourably with established techniques, is robust to noise, and easily avoids overfitting.",
-
notes = "Proceedings of the Workshop on Artificial Intelligence
and Simulation of Behaviour Workshop on Evolutionary
Computing. Workshop in Leeds, UK, April 11-13,
1994
OAKPAPRS.TARR.GZ contains: AISBLNCS.TEX - TeX format, but without the illustrations. If you want to see the pictures, then you must get hold of a copy of the original.",
