Continuous probabilistic model building genetic network programming using reinforcement learning
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- @Article{Li:2015:ASC,
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author = "Xianneng Li and Kotaro Hirasawa",
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title = "Continuous probabilistic model building genetic
network programming using reinforcement learning",
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journal = "Applied Soft Computing",
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year = "2015",
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volume = "27",
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number = "Supplement C",
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pages = "457--467",
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keywords = "genetic algorithms, genetic programming, genetic
network programming, Estimation of distribution
algorithm, Probabilistic model building, Continuous
optimization, Reinforcement learning",
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ISSN = "1568-4946",
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URL = "http://www.sciencedirect.com/science/article/pii/S156849461400533X",
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DOI = "doi:10.1016/j.asoc.2014.10.023",
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abstract = "Recently, a novel probabilistic model-building
evolutionary algorithm (so called estimation of
distribution algorithm, or EDA), named probabilistic
model building genetic network programming (PMBGNP),
has been proposed. PMBGNP uses graph structures for its
individual representation, which shows higher
expression ability than the classical EDAs. Hence, it
extends EDAs to solve a range of problems, such as data
mining and agent control. This paper is dedicated to
propose a continuous version of PMBGNP for continuous
optimization in agent control problems. Different from
the other continuous EDAs, the proposed algorithm
evolves the continuous variables by reinforcement
learning (RL). We compare the performance with several
state-of-the-art algorithms on a real mobile robot
control problem. The results show that the proposed
algorithm outperforms the others with statistically
significant differences.",
- }
Genetic Programming entries for
Xianneng Li
Kotaro Hirasawa
Citations