- 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",
- size = "22 pages",
- 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
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",
