Revisiting the Sequential Symbolic Regression Genetic Programming
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- @InProceedings{Oliveira:2016:BRACIS,
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author = "Luiz Otavio V. B. Oliveira and
Fernando E. B. Otero and Luis F. Miranda and Gisele L. Pappa",
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booktitle = "2016 5th Brazilian Conference on Intelligent Systems
(BRACIS)",
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title = "Revisiting the Sequential Symbolic Regression Genetic
Programming",
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year = "2016",
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pages = "163--168",
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abstract = "Sequential Symbolic Regression (SSR) is a technique
that recursively induces functions over the error of
the current solution, concatenating them in an attempt
to reduce the error of the resulting model. As proof of
concept, the method was previously evaluated in
one-dimensional problems and compared with canonical
Genetic Programming (GP) and Geometric Semantic Genetic
Programming (GSGP). In this paper we revisit SSR
exploring the method behaviour in higher dimensional,
larger and more heterogeneous datasets. We discuss the
difficulties arising from the application of the method
to more complex problems, e.g., over fitting, along
with suggestions to overcome them. An experimental
analysis was conducted comparing SSR to GP and GSGP,
showing SSR solutions are smaller than those generated
by the GSGP with similar performance and more accurate
than those generated by the canonical GP.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/BRACIS.2016.039",
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month = oct,
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notes = "Also known as \cite{7839580}",
- }
Genetic Programming entries for
Luiz Otavio Vilas Boas Oliveira
Fernando Esteban Barril Otero
Luis Fernando Miranda
Gisele L Pappa
Citations