6 citations · 15 across the 10 of their papers we have counts for
11 papers
Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks
Fergal Stapleton, Daniel García Núñez, Yanan Sun +1
State-of-the-art Deep Neural Networks (DNNs) often incorporate multi-branch connections, enabling multi-scale feature extraction and enhancing the capture of diverse features. This…
NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks
Fergal Stapleton, Edgar Galván
Evolutionary Algorithms (EAs) play a crucial role in the architectural configuration and training of Artificial Deep Neural Networks (DNNs), a process known as neuroevolution. Howe…
NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming
Fergal Stapleton, Brendan Cody-Kenny, Edgar Galván
Evolutionary algorithms are increasingly recognised as a viable computational approach for the automated optimisation of deep neural networks (DNNs) within artificial intelligence.…
Evolutionary Multi-objective Optimisation in Neurotrajectory Prediction
Edgar Galván, Fergal Stapleton
Machine learning has rapidly evolved during the last decade, achieving expert human performance on notoriously challenging problems such as image classification. This success is pa…
Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares
Fergal Stapleton, Edgar Galván
Surrogate-assisted evolutionary algorithms (SAEAs) aim to use efficient computational models with the goal of approximating the fitness function in evolutionary computation systems…
Highlights of Semantics in Multi-objective Genetic Programming
Edgar Galván, Leonardo Trujillo, Fergal Stapleton
Semantics is a growing area of research in Genetic programming (GP) and refers to the behavioural output of a Genetic Programming individual when executed. This research expands up…