45 citations · 118 across the 9 of their papers we have counts for
10 papers
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…
BOP-Elites, a Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions
Paul Kent, Adam Gaier, Jean-Baptiste Mouret +1
Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find many high performing points that all behave differently according t…
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…
Weight Agnostic Neural Networks
Adam Gaier, David Ha
Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network comp…
Data-Efficient Design Exploration through Surrogate-Assisted Illumination
Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP…
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…