Genetic-based search for error-correcting graph isomorphism

Yuan Kai Wang; Kuo Chin Fan; Jorng Tzong Horng

doi:10.1109/3477.604100

Genetic-based search for error-correcting graph isomorphism

Yuan Kai Wang, Kuo Chin Fan, Jorng Tzong Horng

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

Abstract

Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic algorithms, some local search strategies are amalgamated to improve convergence speed. Besides, a selection operator is proposed to prevent premature convergence. The proposed approach has been implemented to verify its validity. Experimental results reveal the superiority of this new technique than several other well-known algorithms.

Original language	English
Pages (from-to)	588-597
Number of pages	10
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	27
Issue number	4
DOIs	https://doi.org/10.1109/3477.604100
State	Published - 1997

Keywords

Error-correcting graph isomorphism
Genetic algorithms

Access to Document

10.1109/3477.604100

Cite this

@article{66c7ac45ea4c427690ec1b5433eaf344,

title = "Genetic-based search for error-correcting graph isomorphism",

abstract = "Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic algorithms, some local search strategies are amalgamated to improve convergence speed. Besides, a selection operator is proposed to prevent premature convergence. The proposed approach has been implemented to verify its validity. Experimental results reveal the superiority of this new technique than several other well-known algorithms.",

keywords = "Error-correcting graph isomorphism, Genetic algorithms",

author = "Wang, {Yuan Kai} and Fan, {Kuo Chin} and Horng, {Jorng Tzong}",

note = "Funding Information: Manuscript received April 24, 1994; revised October 7, 1995 and May 19, 1996. This work was supported in part by the National Science Council, R.O.C., under Contract NSC 83-0408-E-008-001. The authors are with the Institute of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan, R.O.C. Publisher Item Identifier S 1083-4419(97)03876-4.",

year = "1997",

doi = "10.1109/3477.604100",

language = "???core.languages.en_GB???",

volume = "27",

pages = "588--597",

journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",

issn = "1083-4419",

number = "4",

}

TY - JOUR

T1 - Genetic-based search for error-correcting graph isomorphism

AU - Wang, Yuan Kai

AU - Fan, Kuo Chin

AU - Horng, Jorng Tzong

N1 - Funding Information: Manuscript received April 24, 1994; revised October 7, 1995 and May 19, 1996. This work was supported in part by the National Science Council, R.O.C., under Contract NSC 83-0408-E-008-001. The authors are with the Institute of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan, R.O.C. Publisher Item Identifier S 1083-4419(97)03876-4.

PY - 1997

Y1 - 1997

N2 - Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic algorithms, some local search strategies are amalgamated to improve convergence speed. Besides, a selection operator is proposed to prevent premature convergence. The proposed approach has been implemented to verify its validity. Experimental results reveal the superiority of this new technique than several other well-known algorithms.

AB - Error-correcting graph isomorphism has been found useful in numerous pattern recognition applications. This paper presents a genetic-based search approach that adopts genetic algorithms as the searching criteria to solve the problem of error-correcting graph isomorphism. By applying genetic algorithms, some local search strategies are amalgamated to improve convergence speed. Besides, a selection operator is proposed to prevent premature convergence. The proposed approach has been implemented to verify its validity. Experimental results reveal the superiority of this new technique than several other well-known algorithms.

KW - Error-correcting graph isomorphism

KW - Genetic algorithms

UR - http://www.scopus.com/inward/record.url?scp=0031207294&partnerID=8YFLogxK

U2 - 10.1109/3477.604100

DO - 10.1109/3477.604100

M3 - 期刊論文

C2 - 18255899

AN - SCOPUS:0031207294

SN - 1083-4419

VL - 27

SP - 588

EP - 597

JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IS - 4

ER -

Genetic-based search for error-correcting graph isomorphism

Abstract

Keywords

Access to Document

Fingerprint

Cite this