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Grammatical evolution for constraint synthesis for mixed-integer linear programming
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DOI：10.1016/j.swevo.2021.100896.png)
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Abstract
En 中文
The Mixed-Integer Linear Programming models are a common representation of real-world objects. They support simulation within the expressed bounds using constraints and optimization of an objective function. Unfortu-nately, handcrafting a model that aligns well with reality is time-consuming and error-prone. In this work, we propose a Grammatical Evolution for Constraint Synthesis (GECS) algorithm that helps human experts by synthe-sizing constraints for Mixed-Integer Linear Programming models. Given relatively easy-to-provide data of avail-able variables and parameters, and examples of feasible solutions, GECS produces a well-formed Mixed-Integer Linear Programming model in the ZIMPL modeling language. GECS outperforms several previous algorithms, copes well with tens of variables, and seems to be resistant to the curse of dimensionality.
Keywords:
Mathematical programming
Model acquisition
Constraint learning
High-level modeling language
Operations research
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