Bayesian Methods for Efficient Genetic Programming
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- @Article{Zhang:2000:bmeGP,
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author = "Byoung-Tak Zhang",
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title = "Bayesian Methods for Efficient Genetic Programming",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2000",
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volume = "1",
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number = "3",
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pages = "217--242",
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month = jul,
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keywords = "genetic algorithms, genetic programming, Bayesian
genetic programming, probabilistic evolution, adaptive
Occam's razor, incremental data inheritance, parsimony
pressure, data subset selection",
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ISSN = "1389-2576",
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URL = "http://bi.snu.ac.kr/Publications/Journals/International/GPEM1-3.pdf",
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URL = "http://citeseer.ist.psu.edu/455254.html",
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URL = "https://rdcu.be/cT7J0",
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DOI = "doi:10.1023/A:1010010230007",
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size = "26 pages",
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abstract = "A Bayesian framework for genetic programming (GP) is
presented. This is motivated by the observation that
genetic programming iteratively searches populations of
fitter programs and thus the information gained in the
previous generation can be used in the next generation.
The Bayesian GP makes use of Bayes theorem to estimate
the posterior distribution of programs from their prior
distribution and likelihood for the fitness data
observed. Offspring programs are then generated by
sampling from the posterior distribution by genetic
variation operators. We present two GP algorithms
derived from the Bayesian GP framework. One is the
genetic programming with the adaptive Occam razor (AOR)
designed to evolve parsimonious programs. The other is
the genetic programming with incremental data
inheritance (IDI) designed to accelerate evolution by
active selection of fitness cases. A multiagent
learning task is used to demonstrate the effectiveness
of the presented methods. In a series of experiments,
AOR reduced solution complexity by 20 percent and IDI
doubled evolution speed, both without loss of solution
accuracy.",
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notes = "Article ID: 264702",
- }
Genetic Programming entries for
Byoung-Tak Zhang
Citations