Can I read NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks on EtoBox?

NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks by Stapleton, Fergal; Galván, Edgar is a scholarly article available to read on EtoBox.

What is NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks about?

Evolutionary Algorithms (EAs) play a crucial role in the architectural configuration and training of Artificial Deep Neural Networks (DNNs), a process known as neuroevolution. However, neuroevolution is hindered by its inherent computational expense, requiring multiple generations, a large population, and numerous epochs. The most computationally intensive aspect lies in evaluating the fitness function of a single candidate solution. To address this challenge, we employ Surrogate-assisted EAs (SAEAs). While a few SAEAs approaches have been proposed in neuroevolution, none have been applied to truly large DNNs due to issues like intractable information usage. In this work, drawing inspiration from Genetic Programming semantics, we use phenotypic distance vectors, outputted from DNNs, alongside Kriging Partial Least Squares (KPLS), an approach that is effective in handling these large vectors, making them suitable for search. Our proposed approach, named Neuro-Linear Genetic Programming surrogate model (NeuroLGP-SM), efficiently and accurately estimates DNN fitness without the need for complete evaluations. NeuroLGP-SM demonstrates competitive or superior results compared to 12 other

Author

Stapleton, Fergal; Galván, Edgar

Published

2024

Language

EN