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