Unpublished,
Gene Expression Programming: a New Adaptive Algorithm for Solving Problems
(2000)rejected for publication.
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.
