AN EMPIRICAL STUDY OF NON-BINARY GENETIC ALGORITHM-BASED NEURAL APPROACHES FOR CLASSIFICATION

Parag C. Pendharkar; James A. Rodger

AN EMPIRICAL STUDY OF NON-BINARY GENETIC ALGORITHM-BASED NEURAL APPROACHES FOR CLASSIFICATION

Parag C. Pendharkar, James A. Rodger

School of Business Administration (Harrisburg)

Research output: Contribution to conference › Paper › peer-review

17 Scopus citations

Abstract

In this paper, we describe a genetic algorithm (GA) based approach for learning connection weights for an artificial neural network (ANN). We use simulated data sets to compare the GA based approach for learning connection weights against the traditional back-propagation algorithm. Our results indicate that GA based training of ANN has a higher reliability (in terms of over-fitting the training data set) and predictive power than the traditional back-propagation algorithm.

Original language	English (US)
Pages	155-165
Number of pages	11
State	Published - 1999
Event	20th International Conference on Information Systems, ICIS 1999 - Charlotte, United States Duration: Dec 13 1999 → Dec 15 1999

Conference

Conference	20th International Conference on Information Systems, ICIS 1999
Country/Territory	United States
City	Charlotte
Period	12/13/99 → 12/15/99

All Science Journal Classification (ASJC) codes

Computer Science Applications
Computer Networks and Communications
Information Systems

Cite this

@conference{d97eafe9eb294a2dad46195e2c8eaa9a,

title = "AN EMPIRICAL STUDY OF NON-BINARY GENETIC ALGORITHM-BASED NEURAL APPROACHES FOR CLASSIFICATION",

abstract = "In this paper, we describe a genetic algorithm (GA) based approach for learning connection weights for an artificial neural network (ANN). We use simulated data sets to compare the GA based approach for learning connection weights against the traditional back-propagation algorithm. Our results indicate that GA based training of ANN has a higher reliability (in terms of over-fitting the training data set) and predictive power than the traditional back-propagation algorithm.",

author = "Pendharkar, {Parag C.} and Rodger, {James A.}",

note = "Publisher Copyright: {\textcopyright} 1999, Association for Information Systems. All rights reserved.; 20th International Conference on Information Systems, ICIS 1999 ; Conference date: 13-12-1999 Through 15-12-1999",

year = "1999",

language = "English (US)",

pages = "155--165",

}

TY - CONF

T1 - AN EMPIRICAL STUDY OF NON-BINARY GENETIC ALGORITHM-BASED NEURAL APPROACHES FOR CLASSIFICATION

AU - Pendharkar, Parag C.

AU - Rodger, James A.

PY - 1999

Y1 - 1999

N2 - In this paper, we describe a genetic algorithm (GA) based approach for learning connection weights for an artificial neural network (ANN). We use simulated data sets to compare the GA based approach for learning connection weights against the traditional back-propagation algorithm. Our results indicate that GA based training of ANN has a higher reliability (in terms of over-fitting the training data set) and predictive power than the traditional back-propagation algorithm.

AB - In this paper, we describe a genetic algorithm (GA) based approach for learning connection weights for an artificial neural network (ANN). We use simulated data sets to compare the GA based approach for learning connection weights against the traditional back-propagation algorithm. Our results indicate that GA based training of ANN has a higher reliability (in terms of over-fitting the training data set) and predictive power than the traditional back-propagation algorithm.

UR - http://www.scopus.com/inward/record.url?scp=85032266822&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032266822&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85032266822

SP - 155

EP - 165

T2 - 20th International Conference on Information Systems, ICIS 1999

Y2 - 13 December 1999 through 15 December 1999

ER -

AN EMPIRICAL STUDY OF NON-BINARY GENETIC ALGORITHM-BASED NEURAL APPROACHES FOR CLASSIFICATION

Abstract

Conference

All Science Journal Classification (ASJC) codes

Other files and links

Fingerprint

Cite this