Distance-based Kernels for Surrogate Model-based Neuroevolution

J\"org Stork; Martin Zaefferer; Thomas Bartz-Beielstein

arxiv: 1807.07839 · v1 · pith:P3IWA2O5new · submitted 2018-07-20 · 💻 cs.NE

Distance-based Kernels for Surrogate Model-based Neuroevolution

J\"org Stork , Martin Zaefferer , Thomas Bartz-Beielstein This is my paper

classification 💻 cs.NE

keywords surrogateoptimizationartificialcomparedifferentdifficultdistance-baseddistances

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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.

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Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning
cs.NE 2019-07 unverdicted novelty 5.0

Surrogate model-based neuroevolution with phenotypic kernels and dynamic input sets considerably increases evaluation efficiency in reinforcement learning tasks.