Evolving Neural Network Topologies for Object Recognition

This paper examines the use of genetic algorithms and neural networks to generate neural network topologies. The data set consists of digital images of objects taken from different angles. A successful neural network topology had been trained on this data, so it was investigated whether the genetic algorithm could evolve a neural network topology capable of learning the training data. The genetic algorithm is used to evolve populations of neural network topologies. The neural network is trained using each of the topologies, and the remaining error in training is used to provide a fitness value for each of the topologies. Thus, the fitness function is the neural network itself.