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Predictive Machine Learning of Objective Boundaries for Solving COPs

Simula Research Laboratory, Kristian Augusts Gate 23, 0164 Oslo, Norway
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Academic Editor: Bernd R. Noack
AI 2021, 2(4), 527-551; https://doi.org/10.3390/ai2040033
Received: 28 September 2021 / Revised: 22 October 2021 / Accepted: 24 October 2021 / Published: 28 October 2021
Solving Constraint Optimization Problems (COPs) can be dramatically simplified by boundary estimation, that is providing tight boundaries of cost functions. By feeding a supervised Machine Learning (ML) model with data composed of the known boundaries and extracted features of COPs, it is possible to train the model to estimate the boundaries of a new COP instance. In this paper, we first give an overview of the existing body of knowledge on ML for Constraint Programming (CP), which learns from problem instances. Second, we introduce a boundary estimation framework that is applied as a tool to support a CP solver. Within this framework, different ML models are discussed and evaluated regarding their suitability for boundary estimation, and countermeasures to avoid unfeasible estimations that avoid the solver finding an optimal solution are shown. Third, we present an experimental study with distinct CP solvers on seven COPs. Our results show that near-optimal boundaries can be learned for these COPs with only little overhead. These estimated boundaries reduce the objective domain size by 60-88% and can help the solver find near-optimal solutions early during the search. View Full-Text
Keywords: constraint programming; predictive machine learning; boundary estimation; constraint optimization constraint programming; predictive machine learning; boundary estimation; constraint optimization
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MDPI and ACS Style

Spieker, H.; Gotlieb, A. Predictive Machine Learning of Objective Boundaries for Solving COPs. AI 2021, 2, 527-551. https://doi.org/10.3390/ai2040033

AMA Style

Spieker H, Gotlieb A. Predictive Machine Learning of Objective Boundaries for Solving COPs. AI. 2021; 2(4):527-551. https://doi.org/10.3390/ai2040033

Chicago/Turabian Style

Spieker, Helge, and Arnaud Gotlieb. 2021. "Predictive Machine Learning of Objective Boundaries for Solving COPs" AI 2, no. 4: 527-551. https://doi.org/10.3390/ai2040033

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