86 citations · 136 across the 21 of their papers we have counts for
9 papers · 1 filter
Behavior-based Neuroevolutionary Training in Reinforcement Learning
Jörg Stork, Martin Zaefferer, Nils Eisler +3
In addition to their undisputed success in solving classical optimization problems, neuroevolutionary and population-based algorithms have become an alternative to standard reinfor…
Continuous Optimization Benchmarks by Simulation
Martin Zaefferer, Frederik Rehbach
Benchmark experiments are required to test, compare, tune, and understand optimization algorithms. Ideally, benchmark problems closely reflect real-world problem behavior. Yet, rea…
Expected Improvement versus Predicted Value in Surrogate-Based Optimization
Frederik Rehbach, Martin Zaefferer, Boris Naujoks +1
Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to decide which point to evaluate next. When Kriging is used as the surrogate model of choi…
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning
Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein +1
In the last years, reinforcement learning received a lot of attention. One method to solve reinforcement learning tasks is Neuroevolution, where neural networks are optimized by ev…
Prediction of neural network performance by phenotypic modeling
Alexander Hagg, Martin Zaefferer, Jörg Stork +1
Surrogate models are used to reduce the burden of expensive-to-evaluate objective functions in optimization. By creating models which map genomes to objective values, these models…
Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels
Jörg Stork, Martin Zaefferer, Thomas Bartz-Beielstein
In NeuroEvolution, the topologies of artificial neural networks are optimized with evolutionary algorithms to solve tasks in data regression, data classification, or reinforcement…
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…
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…