Forecasting Euro - United States Dollar Exchange Rate with Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Maria Antoniou and Efstratios Georgopoulos and 
                 Konstantinos Theofilatos and Spiridon Likothanassis",
  title =        "Forecasting Euro - United States Dollar Exchange Rate
                 with Gene Expression Programming",
  booktitle =    "6th IFIP Advances in Information and Communication
                 Technology AIAI 2010",
  year =         "2010",
  editor =       "Harris Papadopoulos and Andreas Andreou and 
                 Max Bramer",
  volume =       "339",
  series =       "IFIP Advances in Information and Communication
  pages =        "78--85",
  address =      "Larnaca, Cyprus",
  month =        oct # " 6-7",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  DOI =          "doi:10.1007/978-3-642-16239-8_13",
  abstract =     "In the current paper we present the application of our
                 Gene Expression Programming Environment in forecasting
                 Euro-United States Dollar exchange rate. Specifically,
                 using the GEP Environment we tried to forecast the
                 value of the exchange rate using its previous values.
                 The data for the EURO-USD exchange rate are online
                 available from the European Central Bank (ECB). The
                 environment was developed using the JAVA programming
                 language, and is an implementation of a variation of
                 Gene Expression Programming. Gene Expression
                 Programming (GEP) is a new evolutionary algorithm that
                 evolves computer programs (they can take many forms:
                 mathematical expressions, neural networks, decision
                 trees, polynomial constructs, logical expressions, and
                 so on). The computer programs of GEP, irrespective of
                 their complexity, are all encoded in linear
                 chromosomes. Then the linear chromosomes are expressed
                 or translated into expression trees (branched
                 structures). Thus, in GEP, the genotype (the linear
                 chromosomes) and the phenotype (the expression trees)
                 are different entities (both structurally and
                 functionally). This is the main difference between GEP
                 and classical tree based Genetic Programming
  affiliation =  "Pattern Recognition Laboratory, Dept. of Computer
                 Engineering & Informatics, University of Patras, 26500
                 Patras, Greece",
  notes =        "",

Genetic Programming entries for Maria A Antoniou Efstratios F Georgopoulos Konstantinos A Theofilatos Spiridon D Likothanassis