An extended probabilistic model building genetic network programming using both of good and bad individuals
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- @Article{Li:2013:TEEE,
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author = "Xianneng Li and Shingo Mabu and Kotaro Hirasawa",
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title = "An extended probabilistic model building genetic
network programming using both of good and bad
individuals",
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journal = "IEEJ Transactions on Electrical and Electronic
Engineering",
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year = "2013",
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volume = "8",
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number = "4",
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pages = "339--347",
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month = jul,
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publisher = "Wiley",
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keywords = "genetic algorithms, genetic programming, probabilistic
modelling, estimation of distribution algorithms
(EDAs), bad individuals, reinforcement learning,
probabilistic model building genetic network
programming",
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ISSN = "1931-4981",
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DOI = "doi:10.1002/tee.21864",
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size = "9 pages",
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abstract = "Classical estimation of distribution algorithms (EDAs)
generally use truncation selection to estimate the
distribution of the good individuals while ignoring the
bad ones. However, various researches in evolutionary
algorithms (EAs) have reported that the bad individuals
may affect and help solving the problem. This paper
proposes a new method to use the bad individuals by
studying the substructures rather than the entire
individual structures to solve reinforcement learning
(RL) problems, which generally factorise their entire
solutions to the sequences of state-action pairs. This
work was studied in a recent graph-based EDA named
probabilistic model building genetic network
programming (PMBGNP), which could solve RL problems
successfully, to propose an extended PMBGNP. The
effectiveness of this work is verified in an RL
problem, namely robot control. Compared to other
related work, results show that the proposed method can
significantly speed up the evolution efficiency.",
- }
Genetic Programming entries for
Xianneng Li
Shingo Mabu
Kotaro Hirasawa
Citations