A Survey of Statistical Machine Learning Elements in Genetic Programming
Alexandros Agapitos, Róisín Loughran, Miguel Nicolau, Simon Mark Lucas, Michael O’Neill, Anthony Brabazon · IEEE Transactions on Evolutionary Computation · 2019
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.