An improved multi-expression programming algorithm applied in function discovery and data prediction
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- @Article{Zhang:2013:IJICT,
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title = "An improved multi-expression programming algorithm
applied in function discovery and data prediction",
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author = "Qingke Zhang and Bo Yang and Lin Wang and
Jianzhang Jiang",
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journal = "International Journal of Information and Communication
Technology",
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year = "2013",
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month = dec # "~19",
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volume = "5",
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number = "3/4",
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pages = "218--233",
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keywords = "genetic algorithms, genetic programming,
multi-expression programming, MEP, double-layer
chromosome, prediction modelling, function discovery,
data prediction, soft computing, cement strength
prediction.",
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publisher = "Inderscience Publishers",
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language = "eng",
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ISSN = "1741-8070",
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bibsource = "OAI-PMH server at www.inderscience.com",
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URL = "http://www.inderscience.com/link.php?id=54952",
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DOI = "DOI:10.1504/IJICT.2013.054952",
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abstract = "This paper presents an improved multi-expression
programming (MEP). In the algorithm, each individual is
encoded as a double-layer structure, and two-dimension
space operators are introduced through two-dimension
crossover and mutation. The problems of symbolic
expression are defined and used as benchmarks to
compare the effectiveness of proposal method against
the baseline single-layer MEP. Experiments showed that
our method using two-dimensional super chromosome can
find the optimal solution in a short time with small
population. Then the improved algorithm is applied to
the prediction of 28-day cement compressive strength.
Comparison with other three soft computing models,
namely MEP model, neural networks (NN) model and fuzzy
logic (FL) model on cement strength prediction revealed
that the improved MEP model has a lower rate in RMSE
and MAE. Test results demonstrate the proposed method
is efficient and performed better in function discovery
and data prediction.",
- }
Genetic Programming entries for
Qingke Zhang
Bo Yang
Lin Wang
Jianzhang Jiang
Citations