Migration-based multiobjective genetic programming for nonlinear system identification
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- @InProceedings{Ferariu:2009:SACI,
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author = "L. Ferariu and A. Patelli",
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title = "Migration-based multiobjective genetic programming for
nonlinear system identification",
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booktitle = "5th International Symposium on Applied Computational
Intelligence and Informatics, SACI '09",
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year = "2009",
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month = may,
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pages = "475--480",
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keywords = "genetic algorithms, genetic programming, QR
decomposition, adaptive threshold, convergence speed,
dominance analysis, flexible model structure selection,
migration-based multiobjective genetic programming,
nonlinear system identification, optimization
algorithm, quasi independent subpopulation, tree
encoding, identification, nonlinear control systems,
trees (mathematics)",
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DOI = "doi:10.1109/SACI.2009.5136295",
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abstract = "Nonlinear system identification is addressed by means
of genetic programming. For a flexible selection of
model structure and parameters, a multiobjective
optimization of the tree encoded individuals is carried
out, in terms of accuracy and parsimony. The paper
suggests a new optimization algorithm based on the
evolvement of two quasi-independent subpopulations,
which makes use of a flexible migration scheme with
adaptive thresholds and multiple rates. By efficiently
exploiting the concept of dominance analysis, the
algorithm is able to select compact and accurate
models, with good generalization capabilities. The
approach is compliant with nonlinear models, linear in
parameters. That permits the hybridization with QR
decomposition and the use of enhanced genetic
operators, aimed to increase the algorithm convergence
speed. The performances of the suggested design
procedure are illustrated by the identification of two
nonlinear industrial subsystems.",
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notes = "Also known as \cite{5136295}",
- }
Genetic Programming entries for
Lavinia Ferariu
Alina Patelli
Citations