Land Cover/Land Use Multiclass Classification Using GP with Geometric Semantic Operators

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

  author =       "Mauro Castelli and Sara Silva and 
                 Leonardo Vanneschi and Ana Cabral and Maria J. Vasconcelos and 
                 Luis Catarino and Joao M. B. Carreiras",
  title =        "Land Cover/Land Use Multiclass Classification Using GP
                 with Geometric Semantic Operators",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  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",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "334--343",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_34",
  size =         "10 pages",
  abstract =     "Multiclass classification is a common requirement of
                 many land cover/land use applications, one of the
                 pillars of land science studies. Even though genetic
                 programming has been applied with success to a large
                 number of applications, it is not particularly suited
                 for multi-class classification, thus limiting its use
                 on such studies. In this paper we take a step forward
                 towards filling this gap, investigating the performance
                 of recently defined geometric semantic operators on two
                 land cover/land use multiclass classification problems
                 and also on a benchmark problem. Our results clearly
                 indicate that genetic programming using the new
                 geometric semantic operators outperforms standard
                 genetic programming for all the studied problems, both
                 on training and test data.",
  notes =        "
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",

Genetic Programming entries for Mauro Castelli Sara Silva Leonardo Vanneschi Ana Isabel Rosa Cabral Maria Jose Vasconcelos Luis Catarino Joao M B Carreiras