Grammatical swarm for Artificial Neural Network training
Tapas Si, Arunava De, Anup Kumar Bhattacharjee · 2014
This paper presents a proof of concept for Artificial Neural Network training using Grammatical Swarm. Grammatical Swarm is a variant of Grammatical Evolution. The synaptic weight coefficients of a multilayer feed-forward neural network are evolved using Grammatical Swarm. The synaptic weight coefficients are derived from predefined Backus-Naur Form grammar for real value generation in a specified range. The proposed method is applied to solve XOR problem and compared with the multilayer feed-forward neural network training using Particle Swarm Optimizer, Comprehensive Learning Particle Swarm Optimizer, Differential Evolution and Trigonometric Differential Evolution. The experimental results shows that Grammatical Swarm is able to train the Artificial Neural Network.