Automatic Construction of General Non-Recurrent Neural Network Using Genetic Programming

Abstract

An Artificial Neural Network (ANNs) is a model that has been studied for many years in the hope of achieving human like performance. It was used in many applications like (pattern recognition, signal processing, vision, speech recognition, and decisions making aids and robotics).There are many types of ANNs, most of these types are suffering from some problems such as Convergence, Design and some of these ANNs cannot solve the non-linear problems.Since Genetic Programming (GP) is a machine learning technique used in the automatic induction of computer programs, therefore we used it as automatic system for designing and implementing ANNs trying to overcome the previous problems.In this paper we proposed an approach to build ANNs based on evolutionary computation, which uses the GP to evolve both the architecture and weights of General non-recurrent NN simultaneously. New GP modification operations were proposed which are (structure-preserving crossover, structure-preserving mutation, and structure-preserving permutation), these modification operations have clear effects in improving the results of the proposed system.The proposed system has been used in solving five simple problems which are (AND, OR, NOT, Exclusive-OR, and Half-Adder) problems. In addition, we compare the results of the proposed system with Koza work and with three major types of supervised ANNs, which are (Perceptron, Back-Propagation, and Adaline).