Binary Representation in Gene Expression Programming: Towards a Better Scalability

Author

Moreno-Torres, Jose G. ; Llora, X. ; Goldberg, David E.

Author_Institution

Illinois Genetic Algorithms Lab. (IlliGAL), Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA

fYear

2009

fDate

Nov. 30 2009-Dec. 2 2009

Firstpage

1441

Lastpage

1444

Abstract

One of the main problems that arises when using gene expression programming (GEP) conditions in learning classifier systems is the increasing number of symbols present as the problem size grows. When doing model-building LCS, this issue limits the scalability of such a technique, due to the cost required. This paper proposes a binary representation of GEP chromosomes to palliate the computation requirements needed. A theoretical reasoning behind the proposed representation is provided, along with empirical validation.

Keywords

genetic algorithms; pattern classification; GEP chromosomes; binary representation; gene expression programming; learning classifier systems; scalability; Bioinformatics; Biological cells; Encoding; Gene expression; Genomics; Intelligent systems; Mathematical programming; Performance analysis; Scalability; Signal analysis; classifier systems; gene expression programming; genetic algorithms; machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Design and Applications, 2009. ISDA '09. Ninth International Conference on

Conference_Location

Pisa

Print_ISBN

978-1-4244-4735-0

Electronic_ISBN

978-0-7695-3872-3

Type

conf

DOI

10.1109/ISDA.2009.33

Filename

5363972