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• W2800792697 abstract "Mathematical programming (MP) models are common in optimization of real-world processes. Models are usually built by optimization experts in an iterative manner: an imperfect model is continuously improved until it approximates the reality well-enough and meets all technical requirements (e.g., linearity). To facilitate this task, we propose a genetic one-class constraint synthesis method (GOCCS). Given a set of exemplary states of normal operation of a business process, GOCCS synthesizes constraints in linear programming or nonlinear programming form. The synthesized constraints can be then paired with an arbitrary objective function and supplied to an off-the-shelf solver to find optimal parameters of the process. We assess GOCCS on three families of MP benchmarks and conclude promising results. We also apply it to a real-world process of wine production and optimize that process." @default.
• W2800792697 created "2018-05-17" @default.
• W2800792697 creator A5034406933 @default.
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• W2800792697 date "2019-02-01" @default.
• W2800792697 modified "2023-09-23" @default.
• W2800792697 title "Synthesis of Constraints for Mathematical Programming With One-Class Genetic Programming" @default.
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• W2800792697 doi "https://doi.org/10.1109/tevc.2018.2835565" @default.
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