Design of architecture of artificial neural network : design and construction of a model for creation of an architecture of artificial neural network based on distributed genetic algorithms

Abstract

The objective of the work is to design and construct a model for creation of architecture of feed forward artificial neural network. The distributed genetic algorithms are used to design and construct the system. This thesis describes various encoding schemes suggested by researchers for the evolution of architecture of artificial neural network using genetic algorithm. This research proposes new encoding scheme called object� based encoding for the evolution of architecture and also proposes data structures, genetic operators and repair algorithms for the system development. For evolution of weights during training, genetic algorithm is used. For evolution of weights, two dimensional variable length encoding scheme is proposed. For the same, two-point layer crossover and average crossover are proposed. The experiments are carried out on the developed system for the problems like 3-bit even parity. Which combination of genetic operators are more efficient for better design of artificial neural network architecture, is concluded by the experiments.