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Found 9 papers, 1 papers with code
Underwater Acoustic Networks for Security Risk Assessment in Public Drinking Water Reservoirs
We have built a novel system for the surveillance of drinking water reservoirs using underwater sensor networks.
Behavior-based Neuroevolutionary Training in Reinforcement Learning
In addition to their undisputed success in solving classical optimization problems, neuroevolutionary and population-based algorithms have become an alternative to standard reinforcement learning methods.
CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems
The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms.
Surrogate Models for Enhancing the Efficiency of Neuroevolution in Reinforcement Learning
In detail, we investigate a) the potential of SMB-NE with respect to evaluation efficiency and b) how to select adequate input sets for the phenotypic distance measure in a reinforcement learning problem.
Prediction of neural network performance by phenotypic modeling
This difference, the phenotypic distance, can then be used to situate these networks into a common input space, allowing us to produce surrogate models which can predict the performance of neural networks regardless of topology.
Improving NeuroEvolution Efficiency by Surrogate Model-based Optimization with Phenotypic Distance Kernels
For these expensive optimization tasks, surrogate model-based optimization is frequently applied as it features a good evaluation efficiency.
A new Taxonomy of Continuous Global Optimization Algorithms
The extracted features of components or operators allow us to create a set of classification indicators to distinguish between a small number of classes.
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.
Linear Combination of Distance Measures for Surrogate Models in Genetic Programming
We investigate how different genotypic and phenotypic distance measures can be used to learn Kriging models as surrogates.
