45 citations · 118 across the 9 of their papers we have counts for
6 papers · 1 filter
T-DominO: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance Objective
Adam Gaier, James Stoddart, Lorenzo Villaggi +1
Real-world design problems are a messy combination of constraints, objectives, and features. Exploring these problem spaces can be defined as a Multi-Criteria Exploration (MCX) pro…
COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions
Peter J Bentley, Soo Ling Lim, Adam Gaier +1
Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To addre…
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,…
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…
Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination
Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for…