Grammatical evolution for constraint synthesis for mixed-integer linear programming
| Autor: | Tomasz P. Pawlak, Michael O'Neill |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
Constraint learning General Computer Science Linear programming Computer science Modeling language General Mathematics 05 social sciences 050301 education 02 engineering and technology Constraint (information theory) Grammatical evolution 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Representation (mathematics) 0503 education Integer programming Curse of dimensionality |
| Zdroj: | Swarm and Evolutionary Computation. 64:100896 |
| ISSN: | 2210-6502 |
| DOI: | 10.1016/j.swevo.2021.100896 |
| Popis: | 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. Unfortunately, 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 synthesizing constraints for Mixed-Integer Linear Programming models. Given relatively easy-to-provide data of available 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. |
| Databáze: | OpenAIRE |
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