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20162026
most citedModeling User Selection in Quality Diversity

8 citations · 18 across the 5 of their papers we have counts for

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8 papers · 1 filter

cs.NE2026

U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts

Alexander Hagg, Tania Guerrero, Dirk Reith

Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation. Because physics-based climate simulations are computationally expensi…

cs.NE2021

Designing Air Flow with Surrogate-assisted Phenotypic Niching

Alexander Hagg, Dominik Wilde, Alexander Asteroth +1

In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself. But what if we…

cs.NE2021

An Analysis of Phenotypic Diversity in Multi-Solution Optimization

Alexander Hagg, Mike Preuss, Alexander Asteroth +1

More and more, optimization methods are used to find diverse solution sets. We compare solution diversity in multi-objective optimization, multimodal optimization, and quality dive…

cs.NE20198 cited

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…

cs.NE20198 cited

Modeling User Selection in Quality Diversity

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. Qualit…

cs.NE2018

Prototype Discovery using Quality-Diversity

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. Dimens…