Program evolution with explicit learning: a New Framework for Program Automatic Synthesis
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Shan:2003:Pewel,
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author = "Y. Shan and R. I. McKay and H. A. Abbass and
D. Essam",
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title = "Program evolution with explicit learning: a New
Framework for Program Automatic Synthesis",
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booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
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editor = "Ruhul Sarker and Robert Reynolds and
Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and
Tom Gedeon",
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pages = "1639--1646",
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year = "2003",
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publisher = "IEEE Press",
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address = "Canberra",
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publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
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month = "8-12 " # dec,
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organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
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keywords = "genetic algorithms, genetic programming, Ant colony
optimization, Australia, Computer science, Educational
institutions, Probability distribution, Stochastic
processes, Stochastic systems, learning (artificial
intelligence), probability, software prototyping, GP
genetic operators, estimation of distribution
algorithms, evolutionary computing, evolutionary
processing, probability distributions, program
evolution with explicit learning",
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ISBN = "0-7803-7804-0",
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URL = "http://www.cs.adfa.edu.au/~shanyin/publications/peel.pdf",
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URL = "http://citeseer.ist.psu.edu/560804.html",
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DOI = "doi:10.1109/CEC.2003.1299869",
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size = "8 pages",
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abstract = "In genetic programming (GP) and most other
evolutionary computing approaches, the knowledge
learned during the evolutionary processing is
implicitly encoded in the population. A small family of
approaches, known as estimation of distribution
algorithms, learn this knowledge directly in the form
of probability distributions. In this research, we
proposed a new approach for program synthesis - program
evolution with explicit learning (PEEL), belonging to
this family. PEEL learns probability distributions from
previous generations and stochastically generates new
populations according to this distribution. PEEL is
intrinsically different from GP systems because it
abandons conventional GP genetic operators and does not
maintain population. On the benchmark problems we have
studied, this approach shows at least comparable
performance to GP.",
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notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
- }
Genetic Programming entries for
Yin Shan
R I (Bob) McKay
Hussein A Abbass
Daryl Essam
Citations