123 citations · 262 across the 10 of their papers we have counts for
7 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…
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…
A new Taxonomy of Continuous Global Optimization Algorithms
Jörg Stork, A. E. Eiben, Thomas Bartz-Beielstein
Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems.…
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…