- author = "Gabriel Kronberger and Michael Kommenda and Michael Affenzeller",
- title = "Overfitting detection and adaptive covariant parsimony pressure for symbolic regression",
- booktitle = "3rd symbolic regression and modeling workshop for GECCO 2011",
- year = "2011",
- editor = "Steven Gustafson and Ekaterina Vladislavleva",
- isbn13 = "978-1-4503-0690-4",
- keywords = "genetic algorithms, genetic programming",
- pages = "631--638",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
-
DOI = "
doi:10.1145/2001858.2002060",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "Covariant parsimony pressure is a theoretically
motivated method primarily aimed to control bloat. In
this contribution we describe an adaptive method to
control covariant parsimony pressure that is aimed to
reduce overfitting in symbolic regression. The method
is based on the assumption that overfitting can be
reduced by controlling the evolution of program length.
Additionally, we propose an overfitting detection
criterion that is based on the correlation of the
fitness values on the training set and a validation set
of all models in the population.
The proposed method uses covariant parsimony pressure to decrease the average program length when over fitting occurs and allows an increase of the average program length in the absence of overfitting. The proposed approach is applied on two real world datasets. The experimental results show that the correlation of training and validation fitness can be used as an indicator for overfitting and that the proposed method of covariant parsimony pressure adaption alleviates overfitting in symbolic regression experiments with the two datasets.",
-
notes = "Also known as \cite{2002060} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
