Publications (14)
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,…
Generative Design through Quality-Diversity Data Synthesis and Language Models
Adam Gaier, James Stoddart, Lorenzo Villaggi +1
Two fundamental challenges face generative models in engineering applications: the acquisition of high-performing, diverse datasets, and the adherence to precise constraints in gen…
Full Domain Analysis in Fluid Dynamics
Alexander Hagg, Adam Gaier, Dominik Wilde +3
Novel techniques in evolutionary optimization, simulation and machine learning allow for a broad analysis of domains like fluid dynamics, in which computation is expensive and flow…
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…
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…
Language Model Crossover: Variation through Few-Shot Prompting
Elliot Meyerson, Mark J. Nelson, Herbie Bradley +4
This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models o…