TransOpt: Transformer-based Representation Learning for Optimization Problem Classification
This work addresses the challenge of problem classification in black-box optimization for researchers and practitioners, but it is incremental as it applies an existing transformer method to a new domain.
The authors tackled the problem of classifying optimization problem instances by proposing a transformer-based neural network representation, achieving 70%-80% accuracy across different dimensions on the BBOB benchmark.
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.