A geometric semantic genetic programming system for the electoral redistricting problem

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@Article{Castelli:2015:Neurocomputing,
  author =       "Mauro Castelli and Roberto Henriques and 
                 Leonardo Vanneschi",
  title =        "A geometric semantic genetic programming system for
                 the electoral redistricting problem",
  journal =      "Neurocomputing",
  volume =       "154",
  pages =        "200--207",
  year =         "2015",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2014.12.003",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231214016671",
  abstract =     "Redistricting consists in dividing a geographic space
                 or region of spatial units into smaller subregions or
                 districts. In this paper, a Genetic Programming
                 framework that addresses the electoral redistricting
                 problem is proposed. The method uses new genetic
                 operators, called geometric semantic genetic operators,
                 that employ semantic information directly in the
                 evolutionary search process with the objective of
                 improving its optimisation ability. The system is
                 compared to several different redistricting techniques,
                 including evolutionary and non-evolutionary methods.
                 The simulations were made on ten real data-sets and,
                 even though the studied problem does not belong to the
                 classes of problems for which geometric semantic
                 operators induce a unimodal fitness landscape, the
                 results we present demonstrate the effectiveness of the
                 proposed technique.",
  keywords =     "genetic algorithms, genetic programming, Electoral
                 redistricting, Semantics, Search space",
}

Genetic Programming entries for Mauro Castelli Roberto Henriques Leonardo Vanneschi

Citations