Genetic algorithms (GA) are used to optimize the Fast Neutron Source (FNS) core fuel loading to maximize a multiobjective function. The FNS has 150 material locations that can be loaded with one of three different materials resulting in over 3E+71 combinations. The individual designs are evaluated with computationally intensive calls to MCNP. To speed up the optimization, convolutional neural networks (CNN) are trained as surrogate models and used to produce better performing candidates that will meet the design constraints before they are sent to the costly MCNP evaluations. A major hurdle in training neural networks of all kinds is the availability of robust training data. In this application, we use the data produced by the GA as training data for the surrogate models which combine geometric features of the system to predict the objectives and constraint objectives. Utilizing the surrogate models, the accelerated algorithm produced more viable designs that significantly improved the objective function utilizing the same computational resources.

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