- title = "Treating Noisy Data Sets with Relaxed Genetic Programming",
- author = "Luis E. {Da Costa} and Jacques-Andre Landry and Yan Levasseur",
- year = "2007",
- volume = "4926",
- bibdate = "2008-05-16",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ae/ae2007.html#CostaLL07",
- booktitle = "Artificial Evolution",
- editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre Collet and Marc Schoenauer and Evelyne Lutton",
- isbn13 = "978-3-540-79304-5",
- pages = "1--12",
- series = "Lecture Notes in Computer Science",
- address = "Tours, France",
- month = "31-29 " # oct,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1007/978-3-540-79305-2_1",
-
abstract = "In earlier papers we presented a technique (RelaxGP)
for improving the performance of the solutions
generated by Genetic Programming (GP) applied to
regression and approximation of symbolic functions.
RelaxGP changes the definition of a perfect solution:
in standard symbolic regression, a perfect solution
provides exact values for each point in the training
set. RelaxGP allows a perfect solution to belong to a
certain interval around the desired values.
We applied RelaxGP to regression problems where the input data is noisy. This is indeed the case in several real-world problems, where the noise comes, for example, from the imperfection of sensors. We compare the performance of solutions generated by GP and by RelaxGP in the regression of 5 noisy sets. We show that RelaxGP with relaxation values of 10percent to 100percent of the Gaussian noise found in the data can outperform standard GP, both in terms of generalization error reached and in resources required to reach a given test error.",
- notes = "EA'07",
