A GP Approach to Distinguish Chaotic from Noisy Signals

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@InProceedings{kaboudan:1998:GPadcns,

• author = "M. A. Kaboudan",
• title = "A {GP} Approach to Distinguish Chaotic from Noisy Signals",
• booktitle = "Genetic Programming 1998: Proceedings of the Third Annual Conference",
• year = "1998",
• editor = "John R. Koza and Wolfgang Banzhaf and Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and David B. Fogel and Max H. Garzon and David E. Goldberg and Hitoshi Iba and Rick Riolo",
• pages = "187--191",
• address = "University of Wisconsin, Madison, Wisconsin, USA",
• publisher_address = "San Francisco, CA, USA",
• month = "22-25 " # jul,
• publisher = "Morgan Kaufmann",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-55860-548-7",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp1998/kaboudan_1998_GPadcns.pdf",
• size = "5 pages",
• abstract = "We propose a measure of the probability of predicting time series based on genetic programming (GP). The measure is important since GP performs well in predicting deterministic time series while fails on predicting random data. Mixed deterministic and random process must then be at least partially predictable. The proposed measure was tested on artificial data with known but different characteristics. Test results are phenomenological evidence suggest that the measure reasonably approximates a series chance of predictability. it potentially helps reduce model search space, forecasting time and cost. and improve prediction results",
• notes = "GP-98",

}

Genetic Programming entries for Mahmoud A Kaboudan

Citations