A Multi-Level And Multi-Scale Evolutionary Modeling System For Scientific Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Kang:2002:IJCNN,

• author = "Zhou Kang and Yan Li and Hugo {de Garis} and Li-Shan Kang",
• title = "A Multi-Level And Multi-Scale Evolutionary Modeling System For Scientific Data",
• booktitle = "Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN'02",
• pages = "737--742",
• year = "2002",
• month = "12-17 " # may,
• address = "Hilton Hawaiian Village Hotel, Honolulu, Hawaii",
• publisher = "IEEE Press",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• organisation = "IEEE",
• ISBN = "0-7803-7278-6",
• 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",
• DOI = "doi:10.1109/IJCNN.2002.1005565",
• 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.",
• 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