- author = "Alexandros Agapitos and Roisin Loughran and Miguel Nicolau and Simon Lucas and Michael O'Neill and Anthony Brabazon",
- title = "A Survey of Statistical Machine Learning Elements in Genetic Programming",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2019",
- volume = "23",
- number = "6",
- pages = "1029--1048",
- month = dec,
- keywords = "genetic algorithms, genetic programming, Statistical Machine Learning, SML, Generalisation, Overfitting, Classification, Symbolic Regression, Model selection, Regularisation, Model Averaging, Bias-Variance trade-off",
- ISSN = "1089-778X",
-
URL = "
http://ncra.ucd.ie/papers/08648159.pdf",
-
DOI = "
doi:10.1109/TEVC.2019.2900916",
- size = "20 pages",
- abstract = "Modern Genetic Programming operates within the Statistical Machine Learning framework. In this framework evolution needs to balance between approximation of an unknown target function on the training data and generalisation, which is the ability to predict well on new data. The article provides a survey and critical discussion of Statistical Machine Learning methods that enable Genetic Programming to generalise.",
-
notes = "also known as \cite{8648159}
School of Computer Science, University College Dublin, Ireland",
