353 citations
- University of TorontoCA7 papers
- University of CambridgeGB3 papers
- University of Central FloridaUS3 papers
- Amazon (Germany)DE2 papers
- Meta (Israel)IL2 papers
- OpenAI (United States)US2 papers
- University of AmsterdamNL2 papers
- University of WaterlooCA2 papers
- Aalborg UniversityDK1 paper
- Acellent Technologies (United States)US1 paper
- AIT Austrian Institute of Technology GmbHAT1 paper
- Ames Research CenterUS1 paper
Showing cs.NEShow all
3 papers · 1 filter
cs.NE2020★ 42 cited
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang, Joel Lehman, Aditya Rawal +4
Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate pr…
cs.NE2019★ 9 cited
Evolvability ES: Scalable and Direct Optimization of Evolvability
Alexander Gajewski, Jeff Clune, Kenneth O. Stanley +1
Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and e…
cs.NE2019★ 11 cited
Guiding Neuroevolution with Structural Objectives
Kai Olav Ellefsen, Joost Huizinga, Jim Torresen
The structure and performance of neural networks are intimately connected, and by use of evolutionary algorithms, neural network structures optimally adapted to a given task can be…