Genetic Programming Prediction of Solar Activity

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Jagielski:2000:GPP,
author =       "Romuald Jagielski",
title =        "Genetic Programming Prediction of Solar Activity",
booktitle =    "Intelligent Data Engineering and Automated Learning -
IDEAL 2000: Data Mining, Financial Engineering, and
Intelligent Agents",
editor =       "Kwong Sak Leung and Lai-Wan Chan and Helen Meng",
year =         "2000",
series =       "Lecture Notes in Computer Science",
volume =       "1983",
pages =        "199--205",
address =      "Shatin, N.T., Hong Kong, China",
month =        "13-15 " # dec,
publisher =    "Springer-Verlag",
keywords =     "genetic algorithms, genetic programming",
ISBN =         "3-540-41450-9",
CODEN =        "LNCSD9",
ISSN =         "0302-9743",
bibdate =      "Tue Sep 10 19:08:58 MDT 2002",
DOI =          "doi:10.1007/3-540-44491-2_30",
acknowledgement = ack-nhfb,
size =         "7 pages",
abstract =     "For many practical applications, such as planning for
satellite orbits and space missions, it is important to
estimate the future values of the sunspot numbers.
There have been numerous methods used for this
particular case of time series prediction, including
recently neural networks. In this paper we present
genetic programming technique employed to sunspot
series prediction. The paper investigates practical
solutions and heuristics for an effective choice of
parameters and functions of genetic programming. The
results obtained expect the maximum in the current
cycle of the smoothed series monthly sunspot numbers is
$164 \pm 20$, and $162 \pm 20$ for the next cycle
maximum, at the 95% level of confidence. These results
are discussed and compared with other predictions.",
}



Genetic Programming entries for Romuald Jagielski

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