Grass Root Algorithm Optimize Neural Networks for Classification Problem

Abstract

Artificial neural networks are computational models that trying to emulate the structure and functions of biological human networks. They have been extensively used in many applications include science, business, engineering, and data mining. Learning of an artificial neural network means how to adapt the weights of the network interconnections using suitable adaption algorithm. The training algorithms that is used to modify the weights of the network are considered the most important portion that influences the artificial networks performance. In the past few decade, many meta-heuristic algorithms have been used to optimize networks synaptic weights, in order to achieve better performance. This paper proposes a general network training method based on population-based algorithms, proposes a novel meta-heuristic algorithm that is inspired by the general grass plants root system to optimize the weights of the proposed artificial network to classify real data four classes XOR and Iris data comparing the obtained results of the proposed algorithm with other familiar evolutionary meta-heuristic algorithms.