Automatic Modeling of a Novel Gene Expression Programming Based on Statistical Analysis and Critical Velocity
Created by W.Langdon from
gp-bibliography.bib Revision:1.8194
- @InProceedings{Li6:2008:cec,
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author = "Kangshun Li and Weifeng Pan and Wensheng Zhang and
Zhangxin Chen",
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title = "Automatic Modeling of a Novel Gene Expression
Programming Based on Statistical Analysis and Critical
Velocity",
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booktitle = "2008 IEEE World Congress on Computational
Intelligence",
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year = "2008",
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editor = "Jun Wang",
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pages = "169--173",
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address = "Hong Kong",
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month = "1-6 " # jun,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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isbn13 = "978-1-4244-1823-7",
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file = "EC0167.pdf",
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DOI = "doi:10.1109/CEC.2008.4630794",
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abstract = "The basic principle of GEP is briefly introduced. And
considering the defects of classic GEP such as lack of
variety, the problem of convergence and blind searching
without learning mechanism, a novel GEP based on
statistical analysis and stagnancy velocity is proposed
(called AMACGEP). It mainly has the following
characteristics: First, improve the initial population
by statistic analysis of repeated bodies. Second,
introduce the concept of stagnancy velocity to adjust
the searching space, evolution velocity, the diversity
of individuals and the accuracy of prediction. Third,
introduce dynamic mutation operator to improve the
diversity of individuals and the velocity of
convergence. Compared with other methods like
traditional methods, methods of neural network, classic
GEP and other improved GEPs in automatic modelling of
complex function, the simulation results show that the
AMACGEP set up by this paper is better.",
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notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
- }
Genetic Programming entries for
Kangshun Li
Weifeng Pan
Wensheng Zhang
Zhangxin (John) Chen
Citations