文档介绍：Gene Expression Programming: A New Adaptive
Algorithm for Solving Problems
Candidaˆ Ferreira!
Departamento de Cienciasˆ Agrarias,´
Universidade dos Ac¸ores,
9701-851 Terra-Cha˜
Angra do Herosmo, Portugal
Gene expression programming, a genotype/phenotype ic algorithm
(linear and ramified), is presented here for the first time as a new technique
for the creation puter programs. Gene expression programming
uses character linear posed of genes a-
nized in a head and a tail. The chromosomes function as a genome and are
subjected to modification by means of mutation, transposition, root trans-
position, gene transposition, gene bination, and one- and two-point
bination. The chromosomes encode expression trees which are the
object of selection. The creation of these separate entities (genome and ex-
pression tree) with distinct functions allows the algorithm to perform with
high efficiency that greatly surpasses existing adaptive techniques. The
suite of problems chosen to illustrate the power and versatility of gene ex-
pression programming includes symbolic regression, sequence induction
with and without constant creation, block stacking, cellular automata
rules for the density-classification problem, and two problems of boolean
concept learning: the 11-multiplexer and the GP rule problem.
1. Introduction
Gene expression programming (GEP) is, like ic algorithms (GAs)
and ic programming (GP), a ic algorithm as it uses popula-
tions of individuals, selects them according to fitness, and introduces
ic variation using one or more ic operators [1]. The funda-
mental difference between the three algorithms resides in the nature of
the individuals: in GAs the individuals are linear strings of fixed length
(chromosomes); in GP the individuals are nonlinear entities of different
sizes and shapes (parse trees); and in GEP the individuals are encoded
as linear strings of fixed length (the genome or chromosomes) which are
afterwards expresse