Modern genetic programming (GP) operates within the statistical machine learning (SML) framework. In this framework, evolution needs to balance between approximation of an unknown target function on the training data and generalization, which is the ability to predict well on new data. This paper provides a survey and critical discussion of SML methods that enable GP to generalize.
URL: https://ieeexplore.ieee.org/document/8648159
Cite this work
@article{agapitos2019survey, author= {Agapitos, Alexandros and Loughran, Roisin and Nicolau, Miguel and Lucas, Simon and O’Neill, Michael and Brabazon, Anthony}, title= {{A survey of statistical machine learning elements in genetic programming}}, year= {2019}, journal= {{IEEE Transactions on Evolutionary Computation}}, volume= {23}, number= {6}, pages= {1029--1048}, url= {https://ieeexplore.ieee.org/document/8648159}, abstract= {Modern genetic programming (GP) operates within the statistical machine learning (SML) framework. In this framework, evolution needs to balance between approximation of an unknown target function on the training data and generalization, which is the ability to predict well on new data. This paper provides a survey and critical discussion of SML methods that enable GP to generalize.},
}