4 citations · 9 across the 6 of their papers we have counts for
7 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…
Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy
Fred Valdez Ameneyro, Edgar Galvan
Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the MCTS statistical tree is built and th…
Evolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of Carcassonne
Edgar Galván, Gavin Simpson, Fred Valdez Ameneyro
Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the tree is built and the selection proce…
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…
On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne
Edgar Galván, Gavin Simpson
Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The MCTS's popularity is based on its extraordinary results in the challenging two-p…