DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)· 2019· Preprint
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
- 17,374citations
- 2019year
Short summary
This paper introduces a novel method for automatically learning problem instance features directly from high-level representations using a transformer encoder, improving the selection of optimal model-solver combinations for combinatorial optimization.
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Key points
- Developed a machine learning approach to automatically select optimal model-solver combinations for combinatorial optimization.
- Introduced a transformer encoder to learn instance features directly from high-level problem representations.
- Evaluated the approach using the Essence modelling language across three problem classes.
AI-generated from the title and abstract; the full text is not read.
Abstract
Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impact on the effectiveness of the solving process. The best combination of model and solver can also be instance-dependent: there may not exist a single combination that works best for all instances of the same problem. We consider the task of building machine learning models to automatically select the best combination for a problem instance. Critical to the learning process is to define instance features, which serve as input to the selection model. Our contribution is the automatic learning of instance features directly from the high-level representation of a problem instance using a transformer encoder. We evaluate the performance of our approach using the Essence modelling language via a case study of three problem classes.
The authors' abstract, as published at the source. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics), 2019 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science