Gene Expression Programming: a New Adaptive Algorithm for Solving Problems

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

@Unpublished{Ferreira:2000:GEP,
  author =       "Candida Ferreira",
  title =        "Gene Expression Programming: a New Adaptive Algorithm
                 for Solving Problems",
  note =         "rejected for publication",
  year =         "2000",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.gene-expression-programming.com/webpapers/GEP.pdf",
  abstract =     "Gene expression programming, a genome/phenome genetic
                 algorithm (linear and non-linear), is presented here
                 for the first time as a new technique for creation of
                 computer programs. Gene expression programming uses
                 character linear chromosomes composed of genes
                 structurally organised in a head and a tail. The
                 chromosomes function as a genome and are subjected to
                 modification by means of mutation, transposition, root
                 transposition, gene transposition, gene recombination,
                 1-point and 2-point recombination. The chromosomes
                 encode expression trees which are the object of
                 selection. The creation of these separate entities
                 (genome and expression tree) with distinct functions
                 allows the algorithm to perform with high efficiency:
                 in the symbolic regression, sequence induction and
                 block stacking problems it surpasses genetic
                 programming in more than two orders of magnitude,
                 whereas in the density-classification problem it
                 surpasses genetic programming in more than four orders
                 of magnitude. The suite of problems chosen to
                 illustrate the power and versatility of gene expression
                 programming includes, besides the above mentioned
                 problems, two problems of Boolean concept learning: the
                 11-multiplexer and the GP rule problem.",
  notes =        "Date: Tue, 14 Nov 2000 21:04:44 -0100 To:
                 genetic-programming
                 

                 From: Candida Ferreira 
                 Subject: GP: Paper on gene expression programming Hi
                 all,

                 My paper on gene expression programming is now
                 available as a pdf for download at my site:
                 http://www.gene-expression-programming.com

                 Be advised that different versions of this paper were
                 submitted and rejected by Nature and Genetic
                 Programming and Evolvable Machines. One of the reasons
                 one anonymous reviewer from GPEM gave was that The
                 performance of the GEP algorithm compared to GP seems
                 too good to be true to me. As I really want to see
                 other scientists using GEP in other applications, I
                 decided to publish my paper on the web in order to make
                 this powerful algorithm available to all. Remember,
                 though, that there is a patent pending and GEP can not
                 be used commercially. Best regards, Candida Ferreira",
  size =         "pages",
}

Genetic Programming entries for Candida Ferreira

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