Improving Maritime Awareness with Semantic Genetic Programming and Linear Scaling: Prediction of Vessels Position Based on AIS Data

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@InProceedings{Vanneschi:2015:evoApplications,
  author =       "Leonardo Vanneschi and Mauro Castelli and 
                 Ernesto Costa and Alessandro Re and Henrique Vaz and 
                 Victor Lobo and Paulo Urbano",
  title =        "Improving Maritime Awareness with Semantic Genetic
                 Programming and Linear Scaling: Prediction of Vessels
                 Position Based on {AIS} Data",
  booktitle =    "18th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2015",
  editor =       "Antonio M. Mora and Giovanni Squillero",
  series =       "LNCS",
  volume =       "9028",
  publisher =    "Springer",
  pages =        "732--744",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-16548-6",
  DOI =          "doi:10.1007/978-3-319-16549-3_59",
  abstract =     "Maritime domain awareness deals with the situational
                 understanding of maritime activities that could impact
                 the security, safety, economy or environment. It
                 enables quick threat identification, informed decision
                 making, effective action support, knowledge sharing and
                 more accurate situational awareness. In this paper, we
                 propose a novel computational intelligence framework,
                 based on genetic programming, to predict the position
                 of vessels, based on information related to the vessels
                 past positions in a specific time interval. Given the
                 complexity of the task, two well known improvements of
                 genetic programming, namely geometric semantic
                 operators and linear scaling, are integrated in a new
                 and sophisticated genetic programming system. The work
                 has many objectives, for instance assisting more
                 quickly and effectively a vessel when an emergency
                 arises or being able to chase more efficiently a vessel
                 that is accomplishing illegal actions. The proposed
                 system has been compared to two different versions of
                 genetic programming and three non-evolutionary machine
                 learning methods, outperforming all of them on all the
                 studied test cases.",
  notes =        "evoCOMNET+evoRISK EvoApplications2015 held in
                 conjunction with EuroGP'2015, EvoCOP2015 and
                 EvoMusArt2015
                 http://www.evostar.org/2015/cfp_evoapps.php",
}

Genetic Programming entries for Leonardo Vanneschi Mauro Castelli Ernesto Costa Alessandro Re Henrique Vaz Victor Jose de Almeida e Sousa Lobo Paulo Urbano

Citations