{"title":"遗传算法在图匹配中的应用","authors":"M. Krcmár, A. Dhawan","doi":"10.1109/ICNN.1994.374829","DOIUrl":null,"url":null,"abstract":"Genetic algorithms (GA) can be exploited for optimal graph matching. Graphs represent powerful method of a pattern formal description. Globally optimal graph matching is a NP-complete problem. Pattern distortions and noise increase an optimal search difficulty which could be tackled using GA. This paper describes results of simple GA applied on a graph matching problem. As a conclusion, the suitable GA for an optimal graph \"isomorphism\" and \"monomorphism\" is proposed. Used coding resembles the travelling salesman problem (TSP). Consequently, performance of ordering operators has been tested. In contrast to the TSP, the fitness function depends on chromosome value positioning not ordering. It results in differences between optimal GA configuration for graph matching and for TSP.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Application of genetic algorithms in graph matching\",\"authors\":\"M. Krcmár, A. Dhawan\",\"doi\":\"10.1109/ICNN.1994.374829\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Genetic algorithms (GA) can be exploited for optimal graph matching. Graphs represent powerful method of a pattern formal description. Globally optimal graph matching is a NP-complete problem. Pattern distortions and noise increase an optimal search difficulty which could be tackled using GA. This paper describes results of simple GA applied on a graph matching problem. As a conclusion, the suitable GA for an optimal graph \\\"isomorphism\\\" and \\\"monomorphism\\\" is proposed. Used coding resembles the travelling salesman problem (TSP). Consequently, performance of ordering operators has been tested. In contrast to the TSP, the fitness function depends on chromosome value positioning not ordering. It results in differences between optimal GA configuration for graph matching and for TSP.<<ETX>>\",\"PeriodicalId\":209128,\"journal\":{\"name\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNN.1994.374829\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374829","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of genetic algorithms in graph matching
Genetic algorithms (GA) can be exploited for optimal graph matching. Graphs represent powerful method of a pattern formal description. Globally optimal graph matching is a NP-complete problem. Pattern distortions and noise increase an optimal search difficulty which could be tackled using GA. This paper describes results of simple GA applied on a graph matching problem. As a conclusion, the suitable GA for an optimal graph "isomorphism" and "monomorphism" is proposed. Used coding resembles the travelling salesman problem (TSP). Consequently, performance of ordering operators has been tested. In contrast to the TSP, the fitness function depends on chromosome value positioning not ordering. It results in differences between optimal GA configuration for graph matching and for TSP.<>
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