Dynamic sampling in training artificial neural networks with overlapping swarm intelligence
Shehzad Qureshi, John W. Sheppard · 2016
This paper describes an extension to overlapping swarm intelligence for training artificial neural networks. Overlapping swarm intelligence is an application of particle swarm optimization that divides the network into paths from input to output, with each path represented by a swarm. Previous versions of this algorithm showed success on training networks on a variety of datasets but the method suffers from an explosion in fitness evaluations due to the number of paths that need to be evaluated. We propose an extension to overlapping swarm intelligence to use asynchronous updates and dynamic subsets of swarms for each generation, and demonstrate that this method performs as well as basic overlapping swarm intelligence in terms of mean squared error and classification accuracy with fewer fitness evaluations.