Abstract
We identify a novel application of Genetic Programming to automatic synthesis of mathematical programming (MP) models for business processes. Given a set of examples of states of a business process, the proposed Genetic Constraint Synthesis (GenetiCS) method constructs well-formed constraints for an MP model. The form of synthesized constraints (e.g., linear or polynomial) can be chosen accordingly to the nature of the process and the desired type of MP problem. In experimental part, we verify syntactic and semantic fidelity of the synthesized models to the actual benchmark models of varying complexity. The obtained symbolic models of constraints can be combined with an objective function of choice, fed into an off-shelf MP solver, and optimized.
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Notes
- 1.
Technically, we use the MathNet.Symbolics library [20].
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Acknowledgment
T. Pawlak was supported by the statutory activity of Poznan University of Technology, grant no. 09/91/DSMK/0606. K. Krawiec was supported by the National Science Centre, Poland, grant no. 2014/15/B/ST6/05205.
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Pawlak, T.P., Krawiec, K. (2017). Synthesis of Mathematical Programming Constraints with Genetic Programming. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_12
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