Genetic Programming Prediction of Solar Activity

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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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