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Found 3 papers, 1 papers with code
Challenges of ELA-guided Function Evolution using Genetic Programming
Within the optimization community, the question of how to generate new optimization problems has been gaining traction in recent years.
DoE2Vec: Deep-learning Based Features for Exploratory Landscape Analysis
We propose DoE2Vec, a variational autoencoder (VAE)-based methodology to learn optimization landscape characteristics for downstream meta-learning tasks, e. g., automated selection of optimization algorithms.
BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances
Benchmarking is a key aspect of research into optimization algorithms, and as such the way in which the most popular benchmark suites are designed implicitly guides some parts of algorithm design.
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