A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Subgraph isomorphism in planar graphs and related problems
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
An Efficient Algorithm for Graph Isomorphism
Journal of the ACM (JACM)
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Optimization of FPGA configurations using parallel genetic algorithm
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Multi-objective learning via genetic algorithms
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Malware detection based on dependency graph using hybrid genetic algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
An Approximate Maximum Common Subgraph Algorithm for Large Digital Circuits
DSD '10 Proceedings of the 2010 13th Euromicro Conference on Digital System Design: Architectures, Methods and Tools
A new adaptive multi-start technique for combinatorial global optimizations
Operations Research Letters
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In this paper we propose a multi-objective genetic algorithm for the subgraph isomorphism problem. Usually, the number of different edges between two graphs has been used as a fitness function. This approach has limitations in that it only considers directly-visible characteristics of current solutions, not considering the potential for being an optimal solution. We designed a fitness function in which solutions with higher potential can be rated high. This new fitness function has good properties such as transforming the solution space globally convex and improving the performance of local heuristics and genetic algorithms. Experimental results show that the suggested approach brings a considerable improvement in performance and efficiency.