86 citations · 136 across the 21 of their papers we have counts for
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Distance-based Kernels for Surrogate Model-based Neuroevolution
Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein
The topology optimization of artificial neural networks can be particularly difficult if the fitness evaluations require expensive experiments or simulations. For that reason, the…
An Empirical Approach For Probing the Definiteness of Kernels
Martin Zaefferer, Thomas Bartz-Beielstein, Günter Rudolph
Models like support vector machines or Gaussian process regression often require positive semi-definite kernels. These kernels may be based on distance functions. While definitenes…
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming
Martin Zaefferer, Jörg Stork, Oliver Flasch +1
Surrogate models are a well established approach to reduce the number of expensive function evaluations in continuous optimization. In the context of genetic programming, surrogate…
A First Analysis of Kernels for Kriging-based Optimization in Hierarchical Search Spaces
Martin Zaefferer, Daniel Horn
Many real-world optimization problems require significant resources for objective function evaluations. This is a challenge to evolutionary algorithms, as it limits the number of a…