TransOpt: Transformer-based Representation Learning for Optimization Problem Classification

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.