Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University
ELeaRNT: Evolutionary Learning of Rich Neural Network Topologies
The experimental results prove that, in such tasks, our algorithm can build, in a completely automated way, neural network topologies able to outperform classic neural network models designed by hand. Also when applied to classification problems, the performance of the obtained neural networks is fully comparable to that of classic neural networks and in some cases noticeably better.
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