no code implementations • 7 Apr 2023 • Ludovico Scarton, Alexander Hagg
The representation, or encoding, utilized in evolutionary algorithms has a substantial effect on their performance.
1 code implementation • 28 Mar 2023 • Alexander Hagg, Martin L. Kliemank, Alexander Asteroth, Dominik Wilde, Mario C. Bedrunka, Holger Foysi, Dirk Reith
For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints.
1 code implementation • 10 May 2021 • Alexander Hagg, Sebastian Berns, Alexander Asteroth, Simon Colton, Thomas Bäck
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions.
no code implementations • 10 May 2021 • Alexander Hagg, Mike Preuss, Alexander Asteroth, Thomas Bäck
More and more, optimization methods are used to find diverse solution sets.
no code implementations • 10 May 2021 • Alexander Hagg, Dominik Wilde, Alexander Asteroth, Thomas Bäck
In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself.
no code implementations • 16 Jul 2019 • Alexander Hagg, Alexander Asteroth, Thomas Bäck
The initial phase in real world engineering optimization and design is a process of discovery in which not all requirements can be made in advance, or are hard to formalize.
no code implementations • 16 Jul 2019 • Alexander Hagg, Martin Zaefferer, Jörg Stork, Adam Gaier
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.
no code implementations • 25 Jul 2018 • Alexander Hagg, Alexander Asteroth, Thomas Bäck
An iterative computer-aided ideation procedure is introduced, building on recent quality-diversity algorithms, which search for diverse as well as high-performing solutions.
no code implementations • 29 Mar 2017 • Alexander Hagg
Evolutionary illumination is a recent technique that allows producing many diverse, optimal solutions in a map of manually defined features.
no code implementations • 21 Mar 2017 • Alexander Hagg, Maximilian Mensing, Alexander Asteroth
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, which is important when training networks with a small number of samples.
no code implementations • 3 Jun 2016 • Alexander Hagg, Frederik Hegger, Paul Plöger
Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios.