Adaptive optimization for solving a class of subgraph isomorphism problems

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 a 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 the traditional tree search approach