Directing crossover for reduction of bloat in GP

Author

Terrio, M.D. ; Heywood, M.I.

Author_Institution

Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada

Volume

2

fYear

2002

fDate

2002

Firstpage

1111

Abstract

A method is proposed to reduce the amount of invisible code (or bloat) produced in individuals while searching for a parsimonious solution under tree structured genetic programming. Known as directed crossover this process involves the identification of highly fit nodes to use as crossover points during operator application. Three test problems, including medical data classification, are used to assess the performance of directed crossover when applied at various thresholds. Results, collected over 1260 independent runs, identify conditions under which directed crossover reduces code bloat.

Keywords

genetic algorithms; genetics; liver; medical diagnostic computing; trees (mathematics); benchmark medical data sets; breast liver data sets; chromosome crossover; code bloat; directed crossover; highly fit nodes; independent runs; individuals; introns; invisible code; medical data classification; operator application; parsimonious solution; steady state tournament selection; test problems; thresholds; tree structured genetic programming; Bioinformatics; Biological cells; Computer science; Genetic algorithms; Genetic programming; Genomics; Machine learning; Medical tests; Network-on-a-chip; Resource management;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2002. IEEE CCECE 2002. Canadian Conference on

ISSN

0840-7789

Print_ISBN

0-7803-7514-9

Type

conf

DOI

10.1109/CCECE.2002.1013102

Filename

1013102