A Novel Estimation of Distribution Algorithm Using Graph-based Chromosome Representation and Reinforcement Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Li:2011:ANEoDAUGCRaRL,
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title = "A Novel Estimation of Distribution Algorithm Using
Graph-based Chromosome Representation and Reinforcement
Learning",
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author = "Xianneng Li and Bing Li and Shingo Mabu and
Kotaro Hirasawa",
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pages = "37--44",
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booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
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year = "2011",
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editor = "Alice E. Smith",
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month = "5-8 " # jun,
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address = "New Orleans, USA",
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, EDA,
estimation of distribution algorithms, probabilistic
model building genetic network programming",
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DOI = "doi:10.1109/CEC.2011.5949595",
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abstract = "This paper proposed a novel EDA, where a directed
graph network is used to represent its chromosome. In
the proposed algorithm, a probabilistic model is
constructed from the promising individuals of the
current generation using reinforcement learning, and
used to produce the new population. The node connection
probability is studied to develop the probabilistic
model, therefore pairwise interactions can be
demonstrated to identify and recombine building blocks
in the proposed algorithm. The proposed algorithm is
applied to a problem of agent control, i.e., autonomous
robot control. The experimental results show the
superiority of the proposed algorithm comparing with
the conventional algorithms.",
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notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
- }
Genetic Programming entries for
Xianneng Li
Bing Li
Shingo Mabu
Kotaro Hirasawa
Citations