Adaptive optimization for solving a class of subgraph isomorphism problems

Yuan Kai Wang; Kuo Chin Fan; Cheng Wen Liu; Jorng Tzong Horng

Adaptive optimization for solving a class of subgraph isomorphism problems

Yuan Kai Wang, Kuo Chin Fan, Cheng Wen Liu, Jorng Tzong Horng

資訊工程學系

研究成果: 會議貢獻類型 › 會議論文 › 同行評審

3 引文斯高帕斯（Scopus）

摘要

In this paper, genetic algorithms are applied to solve the error-correcting subgraph isomorphism (ECSI) problems. The error-correcting subgraph isomorphism problems are first formulated as permutation searching problems. Two ECSI algorithms are devised. The first algorithm implements pure genetic algorithms with permutation representation. The second is a hybrid algorithm that amalgamates assignment algorithms and local search strategy to improve convergence speed. From experiments, the second algorithm shows better performance than the first one and also reveals that the approach is superior to traditional tree search approach.

原文	???core.languages.en_GB???
頁面	44-48
頁數	5
出版狀態	已出版 - 1995
事件	Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2) - Perth, Aust 持續時間: 29 11月 1995 → 1 12月 1995

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???event.eventtypes.event.conference???	Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2)
城市	Perth, Aust
期間	29/11/95 → 1/12/95

引用此

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abstract = "In this paper, genetic algorithms are applied to solve the error-correcting subgraph isomorphism (ECSI) problems. The error-correcting subgraph isomorphism problems are first formulated as permutation searching problems. Two ECSI algorithms are devised. The first algorithm implements pure genetic algorithms with permutation representation. The second is a hybrid algorithm that amalgamates assignment algorithms and local search strategy to improve convergence speed. From experiments, the second algorithm shows better performance than the first one and also reveals that the approach is superior to traditional tree search approach.",

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AU - Wang, Yuan Kai

AU - Fan, Kuo Chin

AU - Liu, Cheng Wen

AU - Horng, Jorng Tzong

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Y1 - 1995

N2 - In this paper, genetic algorithms are applied to solve the error-correcting subgraph isomorphism (ECSI) problems. The error-correcting subgraph isomorphism problems are first formulated as permutation searching problems. Two ECSI algorithms are devised. The first algorithm implements pure genetic algorithms with permutation representation. The second is a hybrid algorithm that amalgamates assignment algorithms and local search strategy to improve convergence speed. From experiments, the second algorithm shows better performance than the first one and also reveals that the approach is superior to traditional tree search approach.

AB - In this paper, genetic algorithms are applied to solve the error-correcting subgraph isomorphism (ECSI) problems. The error-correcting subgraph isomorphism problems are first formulated as permutation searching problems. Two ECSI algorithms are devised. The first algorithm implements pure genetic algorithms with permutation representation. The second is a hybrid algorithm that amalgamates assignment algorithms and local search strategy to improve convergence speed. From experiments, the second algorithm shows better performance than the first one and also reveals that the approach is superior to traditional tree search approach.

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T2 - Proceedings of the 1995 IEEE International Conference on Evolutionary Computation. Part 1 (of 2)

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Adaptive optimization for solving a class of subgraph isomorphism problems

摘要

???event.eventtypes.event.conference???

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