Improving Gene Expression Programming Method

Najla Al-Saati,Nidhal Al-Assady

doi:10.33899/csmj.2009.163767

Abstract

In this work the algorithm of Gene Expression Programming (GEP) is investigated thoroughly and the major deficiencies are pointed out. Multiple suggestions for enhancements are introduced in this research aiming at solving the major deficiencies that were investigated. These improvements produced higher success rates and avoid the malfunctioning situations found in GEP. These deficiencies or weak points include: choosing the best parameter settings, using only one linking function, gene flattening problem, illegal operations in genes and lack of function biasing. Improvements suggested the following enhancement features: the Multi-Population feature, the Emergency Mutation feature, and the feature of Component Biasing. Tests are carried out using two different symbolic regression problems.

Highlights

Gene Expression Programming (GEP) was introduced by Ferreira in 2001 [5].The great insight of GEP consisted in the invention of chromosomes capable of representing any expression tree
Gene expression programming (GEP) is, like genetic algorithms (GAs) and genetic programming (GP), a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators
In the following tests two equations are used to determine the efficiency of the improvements carried out, they are as indicated in the tables of comparisons: Y = a4 + a3 + a2 + a Fitness cases (Training set) are chosen as those used by all methods proposed so far, this is done to facilitate comparisons

Summary

Introduction

Gene Expression Programming (GEP) was introduced by Ferreira in 2001 [5]. The great insight of GEP consisted in the invention of chromosomes capable of representing any expression tree. AL-Saati and Nidhal Al-Assady functional organization of genes always guarantees the production of valid programs, no matter how much or how profoundly the chromosomes are modified. Gene expression programming (GEP) is, like genetic algorithms (GAs) and genetic programming (GP), a genetic algorithm as it uses populations of individuals, selects them according to fitness, and introduces genetic variation using one or more genetic operators. The genome and phenome mutually presume one another and neither can function without the other. The chromosomes and expression trees of GEP mutually presume one another and neither exists without the other.[5]

Methods

Results

Conclusion