- author = "Gregory Paris and Denis Robilliard and Cyril Fonlupt",
- title = "Exploring Overfitting in Genetic Programming",
- booktitle = "Evolution Artificielle, 6th International Conference",
- year = "2003",
- editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and Evelyne Lutton and Marc Schoenauer",
- volume = "2936",
- series = "Lecture Notes in Computer Science",
- pages = "267--277",
- address = "Marseilles, France",
- month = "27-30 " # oct,
- publisher = "Springer",
- note = "Revised Selected Papers",
- keywords = "genetic algorithms, genetic programming, Artificial Evolution",
- ISBN = "3-540-21523-9",
-
DOI = "
doi:10.1007/b96080",
-
DOI = "doi:10.1007/978-3-540-24621-3_22",
- abstract = "The problem of overfitting (focusing closely on examples at the loss of generalisation power) is encountered in all supervised machine learning schemes. This study is dedicated to explore some aspects of over fitting in the particular case of genetic programming. After recalling the causes usually invoked to explain over-fitting such as hypothesis complexity or noisy learning examples, we test and compare the resistance to over fitting on three variants of genetic programming algorithms (basic GP, sizefair crossover GP and GP with boosting) on two benchmarks, a symbolic regression and a classification problem. We propose guidelines based on these results to help reduce over fitting with genetic programming.",
- bibsource = "DBLP, http://dblp.uni-trier.de",
-
notes = "EA'03
size fair crossover \cite{langdon:2000:fairxo} boosting",
