Evolving robust GP solutions for hedge fund stock selection in emerging markets

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{1277384,
  author =       "Wei Yan and Christopher D. Clack",
  title =        "Evolving robust GP solutions for hedge fund stock
                 selection in emerging markets",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "2234--2241",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2234.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.7354",
  DOI =          "doi:10.1145/1276958.1277384",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Real-World
                 Applications, adaptation, diversity, dynamic
                 environments, finance, phenotype",
  oai =          "oai:CiteSeerXPSU:10.1.1.141.7354",
  abstract =     "Stock selection for hedge fund portfolios is a
                 challenging problem for Genetic Programming (GP)
                 because the markets (the environment in which the GP
                 solution must survive) are dynamic, unpredictable and
                 unforgiving. How can GP be improved so that solutions
                 are produced that are robust to non-trivial changes in
                 the environment? We explore an approach that uses
                 subsets of extreme environments during training.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for Wei Yan Christopher D Clack

Citations