Genetic Network Programming with Estimation of Distribution Algorithms for class association rule mining in traffic prediction
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- @InProceedings{Li:2010:cec,
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author = "Xianneng Li and Shingo Mabu and Huiyu Zhou and
Kaoru Shimada and Kotaro Hirasawa",
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title = "Genetic Network Programming with Estimation of
Distribution Algorithms for class association rule
mining in traffic prediction",
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booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
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year = "2010",
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address = "Barcelona, Spain",
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month = "18-23 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, Genetic
Network Programming",
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isbn13 = "978-1-4244-6910-9",
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abstract = "As an extension of Genetic Algorithm (GA) and Genetic
Programming (GP), a new approach named Genetic Network
Programming (GNP) has been proposed in the evolutionary
computation field. GNP uses multiple reusable nodes to
construct directed-graph structures to represent its
solutions. Recently, many research has clarified that
GNP can work well in data mining area. In this paper, a
novel evolutionary paradigm named GNP with Estimation
of Distribution Algorithms (GNP-EDAs) is proposed and
used to solve traffic prediction problems using class
association rule mining. In GNP-EDAs, a probabilistic
model is constructed by estimating the probability
distribution from the selected elite individuals of the
previous generation to replace the conventional genetic
operators, such as crossover and mutation. The
probabilistic model is capable of enhancing the
evolution to achieve the ultimate objective. In this
paper, two methods are proposed based on extracting the
probabilistic information on the node connections and
node transitions of GNP-EDAs to construct the
probabilistic model. A comparative study of the
proposed paradigm and the conventional GNP is made to
solve the traffic prediction problems using class
association rule mining. The simulation results showed
that GNP-EDAs can extract the class association rules
more effectively, when the number of the candidate
class association rules increases. And the
classification accuracy of the proposed method shows
good results in traffic prediction systems.",
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DOI = "doi:10.1109/CEC.2010.5586456",
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notes = "WCCI 2010. Also known as \cite{5586456}",
- }
Genetic Programming entries for
Xianneng Li
Shingo Mabu
Huiyu Zhou
Kaoru Shimada
Kotaro Hirasawa
Citations