Synthesis of Mathematical Programming Constraints with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Tomasz P. Pawlak and Krzysztof Krawiec",
  title =        "Synthesis of Mathematical Programming Constraints with
                 Genetic Programming",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  month =        "19-21 " # apr,
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  series =       "LNCS",
  volume =       "10196",
  publisher =    "Springer Verlag",
  address =      "Amsterdam",
  pages =        "178--193",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1007/978-3-319-55696-3_12",
  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
  notes =        "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and

Genetic Programming entries for Tomasz Pawlak Krzysztof Krawiec