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Applied Computational Intelligence and Soft Computing
Volume 2010, Article ID 409045, 19 pages
Research Article

Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming

Department of Computer Science, Ryerson University, ON, Canada M5B 2K3

Received 15 September 2009; Revised 6 December 2009; Accepted 11 February 2010

Academic Editor: Oliver Kramer

Copyright © 2010 Nigel P. A. Browne and Marcus V. dos Santos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Gene Expression Programming (GEP) is a genetic algorithm that evolves linear chromosomes encoding nonlinear (tree-like) structures. In the original GEP algorithm, the genome size is problem specific and is determined through trial and error. In this work, a method for adaptive control of the genome size is presented. The approach introduces mutation, transposition, and recombination operators that enable a population of heterogeneously structured chromosomes, something the original GEP algorithm does not support. This permits crossbreeding between normally incompatible individuals, speciation within a population, increases the evolvability of the representations, and enhances parallel GEP. To test our approach, an assortment of problems were used, including symbolic regression, classification, and parameter optimization. Our experimental results show that our approach provides a solution for the problem of self-adaptive control of the genome size of GEP's representation.