1 research outputs found
Distance-based Kernels for Surrogate Model-based Neuroevolution
The topology optimization of artificial neural networks can be particularly
difficult if the fitness evaluations require expensive experiments or
simulations. For that reason, the optimization methods may need to be supported
by surrogate models. We propose different distances for a suitable surrogate
model, and compare them in a simple numerical test scenario.Comment: 4 pages, 1 figure. This publication was accepted to the Developmental
Neural Networks Workshop of the Parallel Problem Solving from Nature 2018
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