TransOpt: Transformer-based Representation Learning for Optimization Problem Classification

• URL: http://arxiv.org/abs/2311.18035v1
• Date: Wed, 29 Nov 2023 19:20:47 GMT
• Title: TransOpt: Transformer-based Representation Learning for Optimization Problem Classification
• Abstract summary: We propose a representation of optimization problem instances using a transformer-based neural network architecture. 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.
• Score: 5.327489950641566
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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