Evolving data classification programs using genetic parallel programming

Author

Cheang, Sin Man ; Lee, Kin Hong ; Leung, Kwong Sak

Author_Institution

Dept. of Comput., Hong Kong Inst. of Vocational Educ., China

Volume

1

fYear

2003

fDate

8-12 Dec. 2003

Firstpage

248

Abstract

A novel linear genetic programming (LGP) paradigm called genetic parallel programming (GPP) has been proposed to evolve parallel programs based on a multi-ALU processor. It is found that GPP can evolve parallel programs for data classification problems. In this paper, five binary-class UCI machine learning repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms.

Keywords

data analysis; genetic algorithms; learning (artificial intelligence); parallel programming; pattern classification; tree data structures; GPP-classifier; UCI machine learning repository databases; classification algorithms; data classification problems; data classification programs; evolutionary process; generalization performance; genetic parallel programming; linear genetic programming paradigm; multiALU processor; parallel algorithms; parallel hardware fitness evaluation; parallel programs; Acceleration; Classification algorithms; Concurrent computing; Data mining; Databases; Genetic programming; Machine learning; Machine learning algorithms; Parallel programming; Registers;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on

Print_ISBN

0-7803-7804-0

Type

conf

DOI

10.1109/CEC.2003.1299582

Filename

1299582