Grammatical Evolution of Robust Controller Structures using Wilson Scoring and Criticality Ranking

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

@InProceedings{Reichensdeorfer:2017:EuroGP,
  author =       "Elias Reichensdeorfer and Dirk Odenthal and 
                 Dirk Wollherr",
  title =        "Grammatical Evolution of Robust Controller Structures
                 using {Wilson} Scoring and Criticality Ranking",
  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 =        "194--209",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  DOI =          "doi:10.1007/978-3-319-55696-3_13",
  abstract =     "In process control it is essential that disturbances
                 and parameter uncertainties do not affect the process
                 in a negative way. Simultaneously optimizing an
                 objective function for different scenarios can be
                 solved in theory by evaluating candidate solutions on
                 all scenarios. This is not feasible in real-world
                 applications, where the scenario space often forms a
                 continuum. A traditional approach is to approximate
                 this evaluation using Monte Carlo sampling. To overcome
                 the difficulty of choosing an appropriate sampling
                 count and to reduce evaluations of low-quality
                 solutions, a novel approach using Wilson scoring and
                 criticality ranking within a grammatical evolution
                 framework is presented. A nonlinear spring mass system
                 is considered as benchmark example from robust control.
                 The method is tested against Monte Carlo sampling and
                 the results are compared to a backstepping controller.
                 It is shown that the method is capable of outperforming
                 state of the art methods.",
  notes =        "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and
                 EvoApplications2017",
}

Genetic Programming entries for Elias Reichensdeorfer Dirk Odenthal Dirk Wollherr

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