Genetic Programming with Memory For Financial Trading

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Alexandros Agapitos and Anthony Brabazon and 
                 Michael O'Neill",
  title =        "Genetic Programming with Memory For Financial
  booktitle =    "19th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2016",
  editor =       "Giovanni Squillero and Paolo Burelli",
  series =       "Lecture Notes in Computer Science",
  volume =       "9597",
  pages =        "19--34",
  address =      "Porto, Portugal",
  month =        mar # " 30 - " # apr # " 1",
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.1007/978-3-319-31204-0_2",
  abstract =     "A memory-enabled program representation in
                 strongly-typed Genetic Programming (GP) is compared
                 against the standard representation in a number of
                 financial time-series modelling tasks. The paper first
                 presents a survey of GP systems that use memory.
                 Thereafter, a number of simulations show that
                 memory-enabled programs generalise better than their
                 standard counterparts in most datasets of this problem
  notes =        "EvoApplications2016 held in conjunction with
                 EuroGP'2016, EvoCOP2016 and EvoMusArt2016",

Genetic Programming entries for Alexandros Agapitos Anthony Brabazon Michael O'Neill