Implicit Fitness Sharing for Evolutionary Synthesis of License Plate Detectors

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Krzysztof Krawiec and Mateusz Nawrocki",
  title =        "Implicit Fitness Sharing for Evolutionary Synthesis of
                 License Plate Detectors",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  volume =       "7835",
  series =       "Lecture Notes in Computer Science",
  pages =        "376--386",
  address =      "Vienna, Austria",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, pattern
                 recognition, image analysis, implicit fitness sharing,
                 car number plate recognition",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_38",
  size =         "11 pages",
  abstract =     "A genetic programming algorithm for synthesis of
                 object detection systems is proposed and applied to the
                 task of license plate recognition in uncontrolled
                 lighting conditions. The method evolves solutions
                 represented as data flows of high-level parametric
                 image operators. In an extended variant, the algorithm
                 employs implicit fitness sharing, which allows
                 identifying the particularly difficult training
                 examples and focusing the training process on them. The
                 experiment, involving heterogeneous video sequences
                 acquired in diverse conditions, demonstrates that
                 implicit fitness sharing substantially improves the
                 predictive performance of evolved detection systems,
                 providing maximum recognition accuracy achievable for
                 the considered setup and training data.",
  notes =        "
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",

Genetic Programming entries for Krzysztof Krawiec Mateusz Nawrocki