Parallel evolutionary training algorithms for "hardware-friendly" neural networks

In this paper, Parallel Evolutionary Algorithms for integer weight neural network training are presented. To this end, each processor is assigned a subpopulation of potential solutions. The subpopulations are independently evolved in parallel and occasional migration is employed to allow cooperation between them. The proposed algorithms are applied to train neural networks using threshold activation functions and weight values confined to a narrow band of integers. We constrain the weights and biases in the range [

• Type: Journal
• Year: 2002
• Where: NC
• Authors: Vassilis P. Plagianakos, Michael N. Vrahatis
• Keywords: Activation Functions, NC 2002, Neural Network, Neural Networks, Parallel Evolutionary Algorithm