An Empirical Study of Functional Complexity as an Indicator of Overfitting in Genetic Programming

Leonardo Trujillo, Sara Silva, Pierrick Legrand, Leonardo Vanneschi

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DOI: 10.1007/978-3-642-20407-4_23
Published: 2011
Container: Lecture Notes in Computer Science
Publisher: Springer Berlin Heidelberg
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/978-3-642-20407-4_23,
  title = {An Empirical Study of Functional Complexity as an Indicator of Overfitting in Genetic Programming},
  author = {Leonardo Trujillo and Sara Silva and Pierrick Legrand and Leonardo Vanneschi},
  year = {2011},
  journal = {Lecture Notes in Computer Science},
  doi = {10.1007/978-3-642-20407-4_23},
  url = {https://doi.org/10.1007/978-3-642-20407-4_23}
}

RIS

TY  - JOUR
TI  - An Empirical Study of Functional Complexity as an Indicator of Overfitting in Genetic Programming
AU  - Leonardo Trujillo
AU  - Sara Silva
AU  - Pierrick Legrand
AU  - Leonardo Vanneschi
PY  - 2011
JO  - Lecture Notes in Computer Science
DO  - 10.1007/978-3-642-20407-4_23
UR  - https://doi.org/10.1007/978-3-642-20407-4_23
ER  -

APA

Trujillo, L., Silva, S., Legrand, P., & Vanneschi, L. (2011). An Empirical Study of Functional Complexity as an Indicator of Overfitting in Genetic Programming. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-20407-4_23

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crossref · retrieved 2026-11-10T20:39:14.062Z