Time Series Prediction Based on Gene Expression                  Programming 
Created by W.Langdon from
gp-bibliography.bib Revision:1.8612
- @InProceedings{DBLP:conf/waim/ZuoTLYC04,
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  author =       "Jie Zuo and Changjie Tang and Chuan Li and 
Chang-an Yuan and An-long Chen",
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  title =        "Time Series Prediction Based on Gene Expression
Programming",
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  booktitle =    "Advances in Web-Age Information Management: 5th
International Conference, WAIM 2004",
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  year =         "2004",
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  pages =        "55--64",
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  bibsource =    "DBLP, http://dblp.uni-trier.de",
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  editor =       "Qing Li and Guoren Wang and Ling Feng",
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  publisher =    "Springer",
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  series =       "Lecture Notes in Computer Science",
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  volume =       "3129",
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  ISBN =         "3-540-22418-1",
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  address =      "Dalian, China",
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  month =        "15-17 " # jul,
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  keywords =     "genetic algorithms, genetic programming, gene
expression programming, Time Series Data Processing",
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  DOI =          " 10.1007/978-3-540-27772-9_7", 10.1007/978-3-540-27772-9_7",
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  abstract =     "Two novel methods for Time Series Prediction based on
GEP (Gene Expression Programming). The main
contributions include: (1) GEP-Sliding Window
Prediction Method (GEP-SWPM) to mine the relationship
between future and historical data directly. (2)
GEP-Differential Equation Prediction Method (GEP-DEPM)
to mine ordinary differential equations from training
data, and predict future trends based on specified
initial conditions. (3) A brand new equation mining
method, called Differential by Microscope Interpolation
(DMI) that boosts the efficiency of our methods. (4) A
new, simple and effective GEP-constants generation
method called Meta-Constants (MC) is proposed. (5) It
is proved that a minimum expression discovered by
GEP-MC method with error not exceeding delta/2 uses at
most log3(2L/delta) operators and the problem to find
delta-accurate expression with fewer operators is
NP-hard. Extensive experiments on real data sets for
sun spot prediction show that the performance of the
new method is 20-900 times higher than existing
algorithms.",
- 
  notes =        "Computer Science Department, Sichuan University,
Chengdu, Sichuan, China, 610065",
- }
Genetic Programming entries for 
Jie Zuo
Changjie Tang
Chuan Li
Chang-an Yuan
An-long Chen
Citations
