Applications of multi-objective structure optimization

We present applications of multi-objective evolutionary optimization of feed-forward neural networks (NN) to two real world problems, car and face classification. The possibly conflicting requirements on the NNs are speed and classification accuracy, both of which can enhance the embedding systems as a whole. We compare the results to the outcome of a greedy optimization heuristic (magnitude-based pruning) coupled with a multi-objective performance evaluation. For the car classification problem, magnitude-based pruning yields competitive results, whereas for the more difficult face classification, we find that the evolutionary approach to NN design is clearly preferable.