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

Joint Authors

dos Santos, Marcus V.
Browne, Nigel P. A.

Source

Applied Computational Intelligence and Soft Computing

Issue

Vol. 2010, Issue 2010 (31 Dec. 2010), pp.1-19, 19 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2010-05-06

Country of Publication

Egypt

No. of Pages

19

Main Subjects

Information Technology and Computer Science

Abstract EN

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.

American Psychological Association (APA)

Browne, Nigel P. A.& dos Santos, Marcus V.. 2010. Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming. Applied Computational Intelligence and Soft Computing،Vol. 2010, no. 2010, pp.1-19.
https://search.emarefa.net/detail/BIM-469840

Modern Language Association (MLA)

Browne, Nigel P. A.& dos Santos, Marcus V.. Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming. Applied Computational Intelligence and Soft Computing No. 2010 (2010), pp.1-19.
https://search.emarefa.net/detail/BIM-469840

American Medical Association (AMA)

Browne, Nigel P. A.& dos Santos, Marcus V.. Adaptive Representations for Improving Evolvability, Parameter Control, and Parallelization of Gene Expression Programming. Applied Computational Intelligence and Soft Computing. 2010. Vol. 2010, no. 2010, pp.1-19.
https://search.emarefa.net/detail/BIM-469840

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-469840