Abstract: Optimization of existing neural networks and optimization of newly defined neural networks is provided. The system starts from an existing neural network with a known state or from a set of desired characteristics for a newly defined neural network and creates a first generation of candidate neural networks with random variations of architectural structures and hyperparameters. Fitness functions are established to evaluate the candidate neural networks. Each candidate neural network is trained and operated and then evaluated using the fitness functions. Top performing architectural structures and hyperparameters are identified and used to create a second generation of candidate neural networks that trained, operated and evaluated. The process iteratively continues until an optimized candidate neural network is determined.