Evolvability Search: Directly Selecting for Evolvability in order to Study and Produce It
By Henok Mengistu, Joel Lehman, Jeff Clune. 2016
34 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sociology and Political Science, Evolutionary Computation
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
- Sergey Levine
- Chelsea Finn
- Pieter Abbeel
- Jeff Clune
- Yoshua Bengio
- Trevor Darrell
- Ruslan Salakhutdinov
- Abhishek Gupta
In Sociology and Political Science
- Pieter Abbeel
- Chelsea Finn
- Sergey Levine
- Max Tegmark
- Jeff Clune
- Kenneth O. Stanley
- Joel Lehman
- Jean-Baptiste Mouret
In Evolutionary Computation
Citations in the corpus
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (2017, methodology): “Similar methods have also been proposed that use evolutionary algorithms (Mengistu et al., 2016).”
- Meta-learning autoencoders for few-shot prediction (2018, methodology): “Other few-shot learning techniques include using Siamese structures [12] and evolutionary methods [14].”
- AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence (2019, background): “In the literature of biological and computational evolution, this phenomenon is called evolvability [85, 86, 190, 29, 130, 189, 110].”