A Novel Graph-Based Estimation of the Distribution Algorithm and its Extension Using Reinforcement Learning
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- @Article{Li:2014:ieeeTEC,
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author = "Xianneng Li and Shingo Mabu and Kotaro Hirasawa",
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title = "A Novel Graph-Based Estimation of the Distribution
Algorithm and its Extension Using Reinforcement
Learning",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2014",
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volume = "18",
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number = "1",
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pages = "98--113",
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month = feb,
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keywords = "genetic algorithms, genetic programming, genetic
network programming, Agent control, estimation of
distribution algorithm (EDA), GNP, graph structure,
reinforcement learning (RL)",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2013.2238240",
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size = "16 pages",
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abstract = "In recent years, numerous studies have drawn the
success of estimation of distribution algorithms (EDAs)
to avoid the frequent breakage of building blocks of
the conventional stochastic genetic operators-based
evolutionary algorithms (EAs). In this paper, a novel
graph-based EDA called probabilistic model building
genetic network programming (PMBGNP) is proposed. Using
the distinguished graph (network) structure of a
graph-based EA called genetic network programming
(GNP), PMBGNP ensures higher expression ability than
the conventional EDAs to solve some specific problems.
Furthermore, an extended algorithm called reinforced
PMBGNP is proposed to combine PMBGNP and reinforcement
learning to enhance the performance in terms of fitness
values, search speed, and reliability. The proposed
algorithms are applied to solve the problems of
controlling the agents' behaviour. Two problems are
selected to demonstrate the effectiveness of the
proposed algorithms, including the benchmark one, i.e.,
the Tileworld system, and a real mobile robot
control.",
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notes = "also known as \cite{6408015}",
- }
Genetic Programming entries for
Xianneng Li
Shingo Mabu
Kotaro Hirasawa
Citations