Coevolution of neural networks using a layered pareto archive

By German A. Monroy, Kenneth O. Stanley, Risto Miikkulainen. 2006

48 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Reinforcement Learning, Neural Architecture Search & AutoML

How researchers see this paper

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

In Artificial Intelligence

1. Shimon Whiteson
2. J. Urgen Schmidhuber
3. Chongjie Zhang
4. Wendelin Böhmer
5. Jakob N. Foerster
6. Christian Schröder de Witt
7. Sergey Levine
8. Tonghan Wang

In Reinforcement Learning

1. Shimon Whiteson
2. Chongjie Zhang
3. Christian Schröder de Witt
4. Jakob N. Foerster
5. Wendelin Böhmer
6. Tonghan Wang
7. Tabish Rashid
8. Stefano V. Albrecht

In Neural Architecture Search & AutoML

1. Shimon Whiteson

Citations in the corpus

• Evolutionary Computation for Reinforcement Learning (2012, methodology): “Using this approach , many methods have been developed that maintain Pareto archives of opponents against which to evaluate evolving solutions (De Jong, 2004; Monroy et al, 2006; De Jong, 2007; Popovici et al, 2010).”