Soft memory for stock market analysis using linear and developmental genetic programming

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

@InProceedings{DBLP:conf/gecco/WilsonB09,
  author =       "Garnett Carl Wilson and Wolfgang Banzhaf",
  title =        "Soft memory for stock market analysis using linear and
                 developmental genetic programming",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1633--1640",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570119",
  abstract =     "Recently, a form of memory usage was introduced for
                 genetic programming (GP) called {"}soft memory.{"}
                 Rather than have a new value completely overwrite the
                 old value in a register, soft memory combines the new
                 and old register values. This work examines the
                 performance of a soft memory linear GP and
                 developmental GP implementation for stock trading. Soft
                 memory is known to more slowly adapt solutions compared
                 to traditional GP. Thus, it was expected to perform
                 well on stock data which typically exhibit local
                 turbulence in combination with an overall longer term
                 trend. While soft memory and standard memory were both
                 found to provide similar impressive accuracy in buys
                 that produced profit and sells that prevented losses,
                 the softer memory settings traded more actively. The
                 trading of the softer memory systems produced less
                 substantial cumulative gains than traditional memory
                 settings for the stocks tested with climbing share
                 price trends. However, the trading activity of the
                 softer memory settings had moderate benefits in terms
                 of cumulative profit compared to buy-and-hold strategy
                 for share price trends involving a drop in prices
                 followed later by gains.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Garnett Carl Wilson Wolfgang Banzhaf

Citations