40 citations · 74 across the 9 of their papers we have counts for
9 papers
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
Muhammad U. Nasir, Sam Earle, Christopher Cleghorn +2
Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have ma…
Training Feedforward Neural Networks with Bayesian Hyper-Heuristics
Arné Schreuder, Anna Bosman, Andries Engelbrecht +1
The process of training feedforward neural networks (FFNNs) can benefit from an automated process where the best heuristic to train the network is sought out automatically by means…
Augmentative Topology Agents For Open-Ended Learning
Muhammad Umair Nasir, Michael Beukman, Steven James +1
In this work, we tackle the problem of open-ended learning by introducing a method that simultaneously evolves agents and increasingly challenging environments. Unlike previous ope…
A Local Optima Network Analysis of the Feedforward Neural Architecture Space
Isak Potgieter, Christopher W. Cleghorn, Anna S. Bosman
This study investigates the use of local optima network (LON) analysis, a derivative of the fitness landscape of candidate solutions, to characterise and visualise the neural archi…
Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels
Michael Beukman, Christopher W Cleghorn, Steven James
Procedurally generated video game content has the potential to drastically reduce the content creation budget of game developers and large studios. However, adoption is hindered by…
Towards Objective Metrics for Procedurally Generated Video Game Levels
Michael Beukman, Steven James, Christopher Cleghorn
With increasing interest in procedural content generation by academia and game developers alike, it is vital that different approaches can be compared fairly. However, evaluating p…
A Continuous Optimisation Benchmark Suite from Neural Network Regression
Katherine M. Malan, Christopher W. Cleghorn
Designing optimisation algorithms that perform well in general requires experimentation on a range of diverse problems. Training neural networks is an optimisation task that has ga…
Point Proposal Network: Accelerating Point Source Detection Through Deep Learning
Duncan Tilley, Christopher W. Cleghorn, Kshitij Thorat +1
Point source detection techniques are used to identify and localise point sources in radio astronomical surveys. With the development of the Square Kilometre Array (SKA) telescope,…
Particle Swarm Optimization: Stability Analysis using N-Informers under Arbitrary Coefficient Distributions
Christopher W Cleghorn, Belinda Stapelberg
This paper derives, under minimal modelling assumptions, a simple to use theorem for obtaining both order- and order- stability criteria for a common class of particle swarm…