Grammatical evolution for constraint synthesis for mixed-integer linear programming

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@Article{journals/swevo/PawlakO21,

• author = "Tomasz P. Pawlak and Michael O'Neill",
• title = "Grammatical evolution for constraint synthesis for mixed-integer linear programming",
• journal = "Swarm and Evolutionary Computation",
• year = "2021",
• volume = "64",
• pages = "100896",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, mathematical programming, model acquisition, constraint learning, high-level modeling language, operations research",
• ISSN = "2210-6502",
• bibdate = "2021-07-29",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/swevo/swevo64.html#PawlakO21",
• URL = "https://www.sciencedirect.com/science/article/pii/S2210650221000572",
• DOI = "doi:10.1016/j.swevo.2021.100896",
• abstract = "The Mixed-Integer Linear Programming models are a common representation of real-world objects. They support simulation within the expressed bounds using constraints and optimisation 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.",

}

Genetic Programming entries for Tomasz Pawlak Michael O'Neill

Citations