Genetic programming: an introduction and survey of applications

By Mark J. Willis, Hugo George Hiden, Peter Marenbach, Ben McKay, Gary A. Montague. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. José Miguel Hernández-Lobato
2. Brooks Paige
3. Matt J. Kusner
4. Zoubin Ghahramani
5. Yarin Gal
6. Dustin Tran
7. Sergey I. Nikolenko
8. Qiang Liu

Citations in the corpus

• Grammar Variational Autoencoder (2017, background): “Learning arithmetic expressions to fit data, often called symbolic regression , are generally based on genetic programming (Willis et al., 1997) or other computationally demanding evolutionary algorithms to propose candidate expressions (Schmidt & Lipson, 2009).”