Publication
Evolving Through the Looking Glass
Learning Improved Search Spaces with Variational Autoencoders
AbstractNature has spent billions of years perfecting our genetic representations, making them evolvable and expressive. Generative machine learning offers a shortcut: learn an evolvable latent space with implicit biases towards better solutions. We present SOLVE: Search space Optimization with Latent Variable Evolution, which creates a dataset of solutions that satisfy extra problem criteria or heuristics, generates a new latent search space, and uses a genetic algorithm to search within this new space to find solutions that meet the overall objective. We investigate SOLVE on five sets of criteria designed to detrimentally affect the search space and explain how this approach can be easily extended as the problems become more complex. We show that, compared to an identical GA using a standard representation, SOLVE with its learned latent representation can meet extra criteria and find solutions with distance to optimal up to two orders of magnitude closer. We demonstrate that SOLVE achieves its results by creating better search spaces that focus on desirable regions, reduce discontinuities, and enable improved search by the genetic algorithm.
Download publicationRelated Resources
03/28/2023
Accelerating Scientific Computing with JAX-LBM
Exploring the fusion of JAX and LBM for ground-breaking research in…
12/06/2022
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
We introduce CAPRI-Net, a self-supervised neural net-work for learning…
01/01/2022
Systems Design and Simulation
Predictive models of complex systems will require a more scalable,…
11/22/2022
Inside Autodesk Research – Exploring our Research Teams
Learn more about Autodesk Research, including our Industry Futures,…
Get in touch
Something pique your interest? Get in touch if you’d like to learn more about Autodesk Research, our projects, people, and potential collaboration opportunities.
Contact us