A Modified Genetic Algorithm for Evolution of Neural Network in Designing an Evolutionary Neuro-Hardware

Artificial Neural Networks (ANNs) are inherently parallel architectures which can be implemented in software and hardware. One important implementation issue is the size of the neural network and its weight adaptation. This makes the hardware implementation complex and software learning slower. In practice Back propagation Neural Network is used for weight learning and evolutionary algorithm for network optimization. In this paper, a modified genetic algorithm with more fondness to mutation is introduced to dynamically evolve network structure and weights at the same time.