Multi-stage genetic programming: A new strategy to nonlinear system modeling
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- @Article{Gandomi20115227,
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author = "Amir Hossein Gandomi and Amir Hossein Alavi",
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title = "Multi-stage genetic programming: A new strategy to
nonlinear system modeling",
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journal = "Information Sciences",
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volume = "181",
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number = "23",
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pages = "5227--5239",
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year = "2011",
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2011.07.026",
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URL = "http://www.sciencedirect.com/science/article/pii/S0020025511003586",
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keywords = "genetic algorithms, genetic programming, Nonlinear
system modelling, Engineering problems, Formulation",
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abstract = "This paper presents a new multi-stage genetic
programming (MSGP) strategy for modelling nonlinear
systems. The proposed strategy is based on
incorporating the individual effect of predictor
variables and the interactions among them to provide
more accurate simulations. According to the MSGP
strategy, an efficient formulation for a problem
comprises different terms. In the first stage of the
MSGP-based analysis, the output variable is formulated
in terms of an influencing variable. Thereafter, the
error between the actual and the predicted value is
formulated in terms of a new variable. Finally, the
interaction term is derived by formulating the
difference between the actual values and the values
predicted by the individually developed terms. The
capabilities of MSGP are illustrated by applying it to
the formulation of different complex engineering
problems. The problems analysed herein include the
following: (i) simulation of pH neutralisation process,
(ii) prediction of surface roughness in end milling,
and (iii) classification of soil liquefaction
conditions. The validity of the proposed strategy is
confirmed by applying the derived models to the parts
of the experimental results that were not included in
the analyses. Further, the external validation of the
models is verified using several statistical criteria
recommended by other researchers. The MSGP-based
solutions are capable of effectively simulating the
nonlinear behaviour of the investigated systems. The
results of MSGP are found to be more accurate than
those of standard GP and artificial neural
network-based models.",
- }
Genetic Programming entries for
A H Gandomi
A H Alavi
Citations