Complex Systems

A Comparison of Several Linear Genetic Programming Techniques Download PDF

Mihai Oltean
Electronic mail address: moltean@nessie.cs.ubbcluj.ro.

Crina Groşan
Electronic mail address: cgrosan@nessie.cs.ubbcluj.ro.
Department of Computer Science,
Faculty of Mathematics and Computer Science,
Babes-Bolyai University, Kogalniceanu 1,
Cluj-Napoca, 3400, Romania

Abstract

A comparison between four Genetic Programming techniques is presented in this paper. The compared methods are Multi-Expression Programming, Gene Expression Programming, Grammatical Evolution, and Linear Genetic Programming. The comparison includes all aspects of the considered evolutionary algorithms: individual representation, fitness assignment, genetic operators, and evolutionary scheme. Several numerical experiments using five benchmarking problems are carried out. Two test problems are taken from PROBEN1 and contain real-world data. The results reveal that Multi-Expression Programming has the best overall behavior for the considered test problems, closely followed by Linear Genetic Programming.