Genetic Programming Algorithms for Dynamic Environments

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/evoW/MacedoCM16,
  author =       "Joao Macedo and Ernesto Costa and Lino Marques",
  title =        "Genetic Programming Algorithms for Dynamic
                 Environments",
  booktitle =    "19th European Conference on Applications of
                 Evolutionary Computation, EvoApplications 2016",
  year =         "2016",
  editor =       "Giovanni Squillero and Paolo Burelli",
  volume =       "9598",
  series =       "Lecture Notes in Computer Science",
  pages =        "280--295",
  address =      "Porto, Portugal",
  month =        mar # " 30 -- " # apr # " 1",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2016-03-29",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-2.html#MacedoCM16",
  isbn13 =       "978-3-319-31153-1",
  URL =          "http://dx.doi.org/10.1007/978-3-319-31153-1",
  DOI =          "doi:10.1007/978-3-319-31153-1_19",
  abstract =     "Evolutionary algorithms are a family of stochastic
                 search heuristics that include Genetic Algorithms (GA)
                 and Genetic Programming (GP). Both GAs and GPs have
                 been successful in many applications, mainly with
                 static scenarios. However, many real world applications
                 involve dynamic environments (DE). Many work has been
                 made to adapt GAs to DEs, but only a few efforts in
                 adapting GPs for this kind of environments. In this
                 paper we present novel GP algorithms for dynamic
                 environments and study their performance using three
                 dynamic benchmark problems, from the areas of Symbolic
                 Regression, Classification and Path Planning.
                 Furthermore, we apply the best algorithm we found in
                 the navigation of an Erratic Robot through a dynamic
                 Santa Fe Ant Trail and compare its performance to the
                 standard GP algorithm. The results, statistically
                 validated, are very promising.",
  notes =        "EvoApplications2016 held inconjunction with
                 EuroGP'2016, EvoCOP2016 and EvoMUSART 2016",
}

Genetic Programming entries for Joao Macedo Ernesto Costa Lino Marques

Citations