DocumentCode :
3061484
Title :
Solving weighted graph matching problem by modified microgenetic algorithm
Author :
Liu, Cheng-Wen ; Fan, Kuo-Chin ; Horng, Jorng-Tzong ; Wang, Yuan-Kai
Author_Institution :
Inst. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-Li, Taiwan
Volume :
1
fYear :
1995
fDate :
22-25 Oct 1995
Firstpage :
638
Abstract :
Microgenetic algorithm (MGA) is genetic algorithm (GA) using a very small population size (population size<20). The weighted graph matching problem (WGMP) has received much attention in the field of pattern recognition. In this paper, a hybrid MGA with larger population is proposed to solved the weighted graph matching problem. In our hybrid microgenetic algorithm, many modules, such as local search algorithm, biased initial population, a modified selection scheme, and a refining procedure, are embedded to improve the performance of the algorithm. Experimental results show that our method outperforms a well-known method, the symmetric polynomial transform (SPT), on most instances of the weighted graph matching problems
Keywords :
genetic algorithms; graph theory; pattern matching; biased initial population; genetic algorithm; local search algorithm; modified microgenetic algorithm; modified selection scheme; pattern recognition; symmetric polynomial transform; weighted graph matching problems; Computer science; Genetic algorithms; Linear programming; Noise robustness; Pattern matching; Pattern recognition; Polynomials; Symmetric matrices; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
Type :
conf
DOI :
10.1109/ICSMC.1995.537835
Filename :
537835
Link To Document :
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