10/7/2002 - The paper presented at the 7th Online World Conference on Soft Computing in Industrial Applications is now available for download at:

https://www.gene-expression-programming.com/author.asp

Ferreira, C., Function Finding and the Creation of Numerical Constants in Gene Expression Programming. 7th Online World Conference on Soft Computing in Industrial Applications, September 23 - October 4, 2002.

ABSTRACT: Gene expression programming is a genotype/phenotype system that evolves computer programs of different sizes and shapes (the phenotype) encoded in linear chromosomes of fixed length (the genotype). The chromosomes are composed of multiple genes, each gene encoding a smaller sub-program. Furthermore, the structural and functional organization of the linear chromosomes allows the unconstrained operation of important genetic operators such as mutation, transposition, and recombination. In this work, three function finding problems, including a high dimensional time series prediction task, are analyzed in an attempt to discuss the question of constant creation in evolutionary computation by comparing two different approaches to the problem of constant creation. The first algorithm involves a facility to manipulate random numerical constants, whereas the second finds the numerical constants on its own or invents new ways of representing them. The results presented here show that evolutionary algorithms perform considerably worse if numerical constants are explicitly used.