A Multi-Level And Multi-Scale Evolutionary Modeling System For Scientific Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Kang:2002:IJCNN,
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author = "Zhou Kang and Yan Li and Hugo {de Garis} and
Li-Shan Kang",
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title = "A Multi-Level And Multi-Scale Evolutionary Modeling
System For Scientific Data",
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booktitle = "Proceedings of the 2002 International Joint Conference
on Neural Networks IJCNN'02",
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pages = "737--742",
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year = "2002",
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month = "12-17 " # may,
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address = "Hilton Hawaiian Village Hotel, Honolulu, Hawaii",
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publisher = "IEEE Press",
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publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
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organisation = "IEEE",
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ISBN = "0-7803-7278-6",
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keywords = "genetic algorithms, genetic programming, KDD, complex
system, data mining, flood season, macroscopic level
laws, microscopic level laws, multilevel multiscale
evolutionary modelling system, natural fractals,
observed time series modelling, observed time series
prediction, ordinary differential equations, scientific
data, scientific law discovery, submicroscopic level
laws, sunspot series, data mining, differential
equations, evolutionary computation, fractals, natural
sciences computing, neural nets",
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DOI = "doi:10.1109/IJCNN.2002.1005565",
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abstract = "The discovery of scientific laws is always built on
the basis of scientific experiments and observed data.
Any real world complex system must be controlled by
some basic laws, including macroscopic level,
submicroscopic level and microscopic level laws. How to
discover its necessity-laws from these observed data is
the most important task of data mining (DM) and KDD.
Based on the evolutionary computation, this paper
proposes a multi-level and multi -scale evolutionary
modeling system which models the macro-behaviour of the
system by ordinary differential equations while models
the micro- behavior of the system by natural fractals.
This system can be used to model and predict the
scientific observed time series, such as observed data
of sunspot and precipitation of flood season, and
always get good results.",
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notes = "IJCNN 2002 Held in connection with the World Congress
on Computational Intelligence (WCCI 2002)",
- }
Genetic Programming entries for
Zhou Kang
Yan Li
Hugo de Garis
Li-Shan Kang
Citations