A linear genetic programming encoding of multi-branch DNN topologies, combined with a semantics-based Kriging surrogate and a pre-selection initialization, improves evolved network accuracy on two of three image datasets at reduced GPU cost.
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Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks
A linear genetic programming encoding of multi-branch DNN topologies, combined with a semantics-based Kriging surrogate and a pre-selection initialization, improves evolved network accuracy on two of three image datasets at reduced GPU cost.