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Adaptive optimization for solving a class of subgraph isomorphism problems

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

Adaptive optimization for solving a class of subgraph isomorphism problems

Abstract

In this paper, genetic algorithms are applied to solve the error-correcting subgraph isomorphism ("I) problems. The error-correcting subgraph isomorphism problems are first formulated as permutation searching problems. Two ECSl 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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Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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