Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space

By Rafal Salustowicz, J. Urgen Schmidhuber. 1997

How researchers see this paper

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

1. Yoshua Bengio
2. Sergey Levine
3. Quoc V. Le
4. Ruslan Salakhutdinov
5. Trevor Darrell
6. Andrea Vedaldi
7. Dacheng Tao
8. Abhinav Kumar Gupta

Citations in the corpus

• Population Based Training of Neural Networks (2017, background): “Finally, there are parallels to work such as Salustowicz & Schmidhuber (1997) which perform program search with genetic algorithms.”
• An introduction and survey of estimation of distribution algorithms (2011, methodology): “Figure 7 shows an example PPT for PIPE.”
• A Survey of Optimization by Building and Using Probabilistic Models (2000, methodology): “In the probabilistic incremental program evolution (PIPE) algorithm [40] computer programs or mathematical functions are evolved like in the genetic programming [20].”