TransOpt: Transformer-based Representation Learning for Optimization
  Problem Classification
Gjorgjina Cenikj Gašper Petelin Tome Eftimov
We propose a representation of optimization problem instances using a
transformer-based neural network architecture trained for the task of problem
classification of the 24 problem classes from the Black-box Optimization
Benchmarking (BBOB) benchmark. We show that transformer-based methods can be
trained to recognize problem classes with accuracies in the range of 70\%-80\%
for different problem dimensions, suggesting the possible application of
transformer architectures in acquiring representations for black-box
optimization problems.