A regressive schema theory based tool for GP evolved nonlinear models
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- @InProceedings{Patelli:2011:ICAC,
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author = "Alina Patelli and Lavinia Ferariu",
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title = "A regressive schema theory based tool for {GP} evolved
nonlinear models",
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booktitle = "17th International Conference on Automation and
Computing (ICAC 2011)",
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year = "2011",
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month = "10 " # sep,
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pages = "201--206",
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address = "Huddersfield, UK",
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keywords = "genetic algorithms, genetic programming, GP,
multi-objective, evolved nonlinear model, fuzzy
logics",
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isbn13 = "978-1-4673-0000-1",
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URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6084927",
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size = "6 pages",
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abstract = "Nonlinear systems identification is approached by
employing a genetic programming computational tool
featuring explicit building block exploitation. The
level of adaptation of recurrent model sub-structures
is assessed by a fuzzy module. The first contribution
of the paper resides in using the fuzzy classification
results to reconfigure the cut point selection
probabilities of regressor inner nodes, a process
called encapsulation. This allows for the second
innovation, namely the design of context aware genetic
operators capable of protecting the existing instances
of fit building blocks and of creating new ones. The
computational costs of encapsulation are reduced by
employing a novel regressive schema theory, the third
and main paper contribution, which assesses the
inherent chances of regressor survival. A thorough
theoretical support for demonstrating the efficiency of
context aware operators in transmitting schema
instances over the generations is introduced. The
suggested algorithm is experimentally validated in the
framework of a complex, industrial, nonlinear subsystem
of a sugar factory.",
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notes = "Also known as \cite{6084927}",
- }
Genetic Programming entries for
Alina Patelli
Lavinia Ferariu
Citations