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Found 10 papers, 0 papers with code
NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks
This efficiency advantage adds to the overall appeal of our proposed NeuroLGP-SM in optimising the configuration of large DNNs.
NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming
The amalgamation of these two techniques culminates in our proposed methodology known as the NeuroLGP-Surrogate Model (NeuroLGP-SM).
Evolutionary Multi-objective Optimisation in Neurotrajectory Prediction
Two well-known and robust Evolutionary Multi-objective Optimisation (EMO) algorithms, NSGA-II and MOEA/D are also adopted.
Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares
This refers to the use of evolutionary algorithms in the automatic configuration of artificial neural network (ANN) architectures, hyper-parameters and/or the training of ANNs.
Highlights of Semantics in Multi-objective Genetic Programming
Semantics is a growing area of research in Genetic programming (GP) and refers to the behavioural output of a Genetic Programming individual when executed.
Neuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles
The incentive for using Evolutionary Algorithms (EAs) for the automated optimization and training of deep neural networks (DNNs), a process referred to as neuroevolution, has gained momentum in recent years.
Semantics in Multi-objective Genetic Programming
This is then used to compute a distance between the pivot and every individual in the population.
Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition
We show, for the first time, how we can promote semantic diversity in MOEA/D in Genetic Programming.
Promoting Semantics in Multi-objective Genetic Programming based on Decomposition
The study of semantics in Genetic Program (GP) deals with the behaviour of a program given a set of inputs and has been widely reported in helping to promote diversity in GP for a range of complex problems ultimately improving evolutionary search.
Semantic-based Distance Approaches in Multi-objective Genetic Programming
We empirically and consistently show how by naturally handling semantic distance as an additional criterion to be optimised in MOGP leads to better performance when compared to canonical methods and SSC.
