57 citations · 125 across the 18 of their papers we have counts for
7 papers · 1 filter
Evolutionary and Coevolutionary Multi-Agent Design Choices and Dynamics
Erik Hemberg, Eric Liu, Lucille Fuller +2
We investigate two representation alternatives for the controllers of teams of cyber agents. We combine these controller representations with different evolutionary algorithms, one…
Guiding Evolutionary AutoEncoder Training with Activation-Based Pruning Operators
Steven Jorgensen, Erik Hemberg, Jamal Toutouh +1
This study explores a novel approach to neural network pruning using evolutionary computation, focusing on simultaneously pruning the encoder and decoder of an autoencoder. We intr…
Evolving Code with A Large Language Model
Erik Hemberg, Stephen Moskal, Una-May O'Reilly
Algorithms that use Large Language Models (LLMs) to evolve code arrived on the Genetic Programming (GP) scene very recently. We present LLM GP, a formalized LLM-based evolutionary…
Analyzing the Components of Distributed Coevolutionary GAN Training
Jamal Toutouh, Erik Hemberg, Una-May O'Reilly
Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maint…
Spatial Evolutionary Generative Adversarial Networks
Jamal Toutouh, Erik Hemberg, Una-May O'Reilly
Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their advers…
Lipizzaner: A System That Scales Robust Generative Adversarial Network Training
Tom Schmiedlechner, Ignavier Ng Zhi Yong, Abdullah Al-Dujaili +2
GANs are difficult to train due to convergence pathologies such as mode and discriminator collapse. We introduce Lipizzaner, an open source software system that allows machine lear…