Keynote Speech 2 - Evolving Deep Neural Networks with Cultural Algorithms for Real-Time Industrial Applications
2022
The goal of this talk is to investigate the applicability of evolutionary algorithms to the design of real-time industrial controllers.Present-day 'deep learning' (DL) is firmly established as a useful tool for addressing many practical problems.This has spurred the development of neural architecture search (NAS) methods in order automate the model search activity.CATNeuro is a NAS algorithm based on the graph evolution concept devised by Neuroevolution of Augmenting Topologies (NEAT) but propelled by Cultural Algorithms (CA) as the evolutionary driver.The CA is a network-based, stochastic optimization framework inspired by problem solving in human cultures.Knowledge distribution across the network of graph models is a key to problem solving success in CAT systems.Two alternative mechanisms for knowledge distribution across the network are employed.One supports cooperation (CAT-NEURO) in the network and the other competition (WM).