Sampling Bias in Estimation of Distribution Algorithms for Genetic Programming Using Prototype Trees
Created by W.Langdon from
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- @InProceedings{conf/pricai/KimMP10,
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title = "Sampling Bias in Estimation of Distribution Algorithms
for Genetic Programming Using Prototype Trees",
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author = "Kangil Kim and R. I. (Bob) McKay and
Dharani Punithan",
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booktitle = "{PRICAI} 2010: Trends in Artificial Intelligence, 11th
Pacific Rim International Conference on Artificial
Intelligence, Daegu, Korea, August 30-September 2,
2010. Proceedings",
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publisher = "Springer",
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year = "2010",
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volume = "6230",
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editor = "Byoung-Tak Zhang and Mehmet A. Orgun",
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isbn13 = "978-3-642-15245-0",
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pages = "100--111",
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series = "Lecture Notes in Computer Science",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://dx.doi.org/10.1007/978-3-642-15246-7",
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DOI = "doi:10.1007/978-3-642-15246-7_12",
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abstract = "Probabilistic models are widely used in evolutionary
and related algorithms. In Genetic Programming (GP),
the Probabilistic Prototype Tree (PPT) is often used as
a model representation. Drift due to sampling bias is a
widely recognised problem, and may be serious,
particularly in dependent probability models. While
this has been closely studied in independent
probability models, and more recently in probabilistic
dependency models, it has received little attention in
systems with strict dependence between probabilistic
variables such as arise in PPT representation. Here, we
investigate this issue, and present results suggesting
that the drift effect in such models may be
particularly severe; so severe as to cast doubt on
their scalability. We present a preliminary analysis
through a factor representation of the joint
probability distribution. We suggest future directions
for research aiming to overcome this problem",
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bibdate = "2010-08-27",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/pricai/pricai2010.html#KimMP10",
- }
Genetic Programming entries for
Kangil Kim
R I (Bob) McKay
Dharani Punithan
Citations