Revisiting the Sequential Symbolic Regression Genetic Programming

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@InProceedings{Oliveira:2016:BRACIS,

• author = "Luiz Otavio V. B. Oliveira and Fernando E. B. Otero and Luis F. Miranda and Gisele L. Pappa",
• booktitle = "2016 5th Brazilian Conference on Intelligent Systems (BRACIS)",
• title = "Revisiting the Sequential Symbolic Regression Genetic Programming",
• year = "2016",
• pages = "163--168",
• 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.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/BRACIS.2016.039",
• month = oct,
• 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