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20172021
most citedModeling User Selection in Quality Diversity

8 citations · 10 across the 2 of their papers we have counts for

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7 papers · 1 filter

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.NE2020

Discovering Representations for Black-box Optimization

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret

The encoding of solutions in black-box optimization is a delicate, handcrafted balance between expressiveness and domain knowledge -- between exploring a wide variety of solutions,…

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…

cs.NE2018

Data-efficient Neuroevolution with Kernel-Based Surrogate Models

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret

Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so…