Probabilistic Model Building GP with Belief Propagation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Sato:2012:CECa,
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title = "Probabilistic Model Building {GP} with Belief
Propagation",
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author = "Hiroyuki Sato and Yoshihiko Hasegawa and
Danushka Bollegala and Hitoshi Iba",
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pages = "2089--2096",
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booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
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year = "2012",
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editor = "Xiaodong Li",
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month = "10-15 " # jun,
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DOI = "doi:10.1109/CEC.2012.6256483",
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address = "Brisbane, Australia",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming, Estimation of
distribution algorithms",
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abstract = "Estimation of distribution algorithms (EDAs) which
deal with tree structures as GP are called as
probabilistic model building GPs (PMBGPs), and they
show better search performance than GP in many
problems. A problem of prototype tree-based method, a
type of PMBGPs, is that samplings do not always
generate the most probable solution, which is the
individual with the highest probability and reflects a
learnt distribution most. This problem wastes a part of
learning and increases the number of evaluations to get
an optimum solution. In order to overcome this
difficulty, this paper proposes a hybrid approach using
Belief propagation (BP) in sampling process. BP is an
inference algorithm on graphical models and can
generate the most probable solution. By applying our
approach to benchmark tests, we show that the proposed
method is more effective than PLS alone.",
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notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
EPS and the IET.",
- }
Genetic Programming entries for
Hiroyuki Sato
Yoshihiko Hasegawa
Danushka Bollegala
Hitoshi Iba
Citations