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Found 11 papers, 6 papers with code
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimization
For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints.
An Analysis of Phenotypic Diversity in Multi-Solution Optimization
More and more, optimization methods are used to find diverse solution sets.
Designing Air Flow with Surrogate-assisted Phenotypic Niching
In complex, expensive optimization domains we often narrowly focus on finding high performing solutions, instead of expanding our understanding of the domain itself.
Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions.
Discovering Representations for Black-box Optimization
Our second insight is that this representation can be used to scale quality diversity optimization to higher dimensions -- but only if we carefully mix solutions generated with the learned representation and those generated with traditional variation operators.
Modeling User Selection in Quality Diversity
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.
Prototype Discovery using Quality-Diversity
An iterative computer-aided ideation procedure is introduced, building on recent quality-diversity algorithms, which search for diverse as well as high-performing solutions.
Data-Efficient Design Exploration through Surrogate-Assisted Illumination
Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs.
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms.
Evolving Parsimonious Networks by Mixing Activation Functions
Neuroevolution methods evolve the weights of a neural network, and in some cases the topology, but little work has been done to analyze the effect of evolving the activation functions of individual nodes on network size, which is important when training networks with a small number of samples.
Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination
The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user.
