The effect of function noise on GP efficiency
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @InCollection{lee:1995:efnGPe,
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author = "Jack Y. B. Lee and P. C. Wong",
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title = "The effect of function noise on GP efficiency",
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booktitle = "Progress in Evolutionary Computation",
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publisher = "Springer-Verlag",
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year = "1995",
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editor = "Xin Yao",
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volume = "956",
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series = "Lecture Notes in Artificial Intelligence",
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pages = "1--16",
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address = "Heidelberg, Germany",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-540-60154-8",
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DOI = "doi:10.1007/3-540-60154-6_43",
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abstract = "Genetic Programming (GP) has been applied to many
problems and there are indications [1,2,3] that GP is
potentially useful in evolving algorithms for problem
solving. This paper investigates one problem with
algorithmic evolution using GP - Function Noise. We
show that the performance of GP could be severely
degraded even in the presence of minor noise in the GP
functions. We investigated two counter noise schemes,
Multi-Sampling Function and Multi-Testcases. We show
that the Multi-Sampling Function scheme can reduce the
effect of noise in a predictable way while the
Multi-Test cases scheme evolves radically different
program structures to avoid the effect of noise.
Essentially, the two schemes lead the GP to evolve into
different approaches to solving the same problem.",
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size = "16 pages",
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notes = "Artificial ant on Santa Fe Trail with noisy
IfFoodAhead GP does poorly even with small amounts of
with noise. Sometimes population abandons use of
IfFoodAhead entirely (what else could it do?)
",
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affiliation = "The Chinese University of Hong Kong Advanced Network
Systems Laboratory Department of Information
Engineering Hongkong Hongkong",
- }
Genetic Programming entries for
Jack Y B Lee
Po Choi Wong
Citations