Multiple response optimization: Analysis of genetic programming for symbolic regression and assessment of desirability functions
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- @Article{GOMES:2019:KS,
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author = "Fabricio M. Gomes and Felix M. Pereira and
Aneirson F. Silva and Messias B. Silva",
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title = "Multiple response optimization: Analysis of genetic
programming for symbolic regression and assessment of
desirability functions",
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journal = "Knowledge-Based Systems",
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volume = "179",
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pages = "21--33",
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year = "2019",
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ISSN = "0950-7051",
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DOI = "doi:10.1016/j.knosys.2019.05.002",
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URL = "http://www.sciencedirect.com/science/article/pii/S0950705119302096",
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keywords = "genetic algorithms, genetic programming, Optimization,
Desirability function, Modeling",
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abstract = "Multiple responses optimization (MRO) consists in the
search for the best settings in an problem with
conflicting responses. MRO is performed following the
steps: experimental design; experimental data
gathering; mathematical models building; statistical
validation of models; agglutination of the models
responses in only one function to be optimized;
optimization of agglutinated function; experimental
validation of the best conditions. This work selected
two MRO cases from literature aiming to compare two
methods of mathematical models building and two
agglutinating functions to assess the best one among
the four possible combinations. The methods used in
mathematical models building were the ordinary least
squares performed in Minitab (v. 17) and genetic
programming performed in Eureqa Formulize (v. 1.24.0).
The assessment of the best method for building
mathematical models was performed using the Akaike
Information Criterion. The responses agglutination were
performed using the desirability and modified
desirability functions. In all MRO cases, the
optimization step was performed by generalized reduced
gradient method on Microsoft ExcelTM software. The
average percentage distance between predicted and
experimental results was used to both assess the best
agglutination function and verify the effect of the
method used in the building of the mathematical models
about its fitness to estimate the best condition close
to that one obtained on experimental validation step.
The obtained results suggest as the better strategy for
multiple responses optimization the use, jointly, of
genetic programming to mathematical models building and
the modified desirability function to responses
agglutination",
- }
Genetic Programming entries for
Fabricio Maciel Gomes
Felix Monteiro Pereira
Aneirson F Silva
Messias Borges Silva
Citations