1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.NE2025
Low Rank Factorizations are Indirect Encodings for Deep Neuroevolution
Jack Garbus, Jordan Pollack
Deep neuroevolution is a highly scalable alternative to reinforcement learning due to its unique ability to encode network updates in a small number of bytes. Recent insights from…
cs.NE2024
Phylogeny-Informed Interaction Estimation Accelerates Co-Evolutionary Learning
Jack Garbus, Thomas Willkens, Alexander Lalejini +1
Co-evolution is a powerful problem-solving approach. However, fitness evaluation in co-evolutionary algorithms can be computationally expensive, as the quality of an individual in…
cs.NE2024★ 1 cited
Runtime phylogenetic analysis enables extreme subsampling for test-based problems
Alexander Lalejini, Marcos Sanson, Jack Garbus +2
A phylogeny describes the evolutionary history of an evolving population. Evolutionary search algorithms can perfectly track the ancestry of candidate solutions, illuminating a pop…