The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Genetic quantum algorithm and its application to combinatorial optimization problem
Abstract
This paper proposes a novel evolutionary computing method called a genetic quantum algorithm (GQA). GQA is based on the concept and principles of quantum computing such as qubits and superposition of states. Instead of binary, numeric, or symbolic representation, by adopting qubit chromosome as a representation GQA can represent a linear superposition of solutions due to its probabilistic representation. As genetic operators, quantum gates are employed for the search of the best solution. Rapid convergence and good global search capability characterize the performance of GQA. The effectiveness and the applicability of GQA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that GQA is superior to other genetic algorithms using penalty functions, repair methods and decoders.
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Citations
Cites
Genetic Algorithms + Data Structures = Evolution Programs
Michalewicz, Z. (1992) · Artificial Intelligence · doi:10.1007/978-3-662-02830-8 · Cited by 11,553
A fast quantum mechanical algorithm for database search
Grover, L. K. (1996) · Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC '96 · doi:10.1145/237814.237866 · Cited by 8,830
Algorithms for quantum computation: discrete logarithms and factoring
Shor, P. W. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365700 · Cited by 8,713
Genetic Algorithms + Data Structures = Evolution Programs
Rocke, D. M., & Michalewicz, Z. (2000) · Journal of the American Statistical Association · doi:10.2307/2669583 · Cited by 880
Quantum-inspired genetic algorithms
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Quantum computing: an introduction
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Finding a better-than-classical quantum AND/OR algorithm using genetic programming
Spector, L. C., Barnum, H., Bernstein, H. J., & Swamy, N. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.785553 · Cited by 116
Quantum computing for beginners
Narayanan, A. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.785552 · Cited by 67
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Cited by
Multiobjective evolutionary algorithms: A survey of the state of the art
Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.03.001 · Cited by 2,255
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
Han, K.-H., & Kim, J.-H. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2002.804320 · Cited by 1,585
Self-citationQuantum-inspired evolutionary algorithms: a survey and empirical study
Zhang, G. (2011) · Journal of Heuristics · doi:10.1007/s10732-010-9136-0 · Cited by 295
Quantum Optimization and Quantum Learning: A Survey
Li, Y., Tian, M., Liu, G., Peng, C., & Jiao, L. (2020) · IEEE Access · doi:10.1109/access.2020.2970105 · Cited by 177
Zhu, Y., Zhang, M., Huang, Q., Wu, X., Wan, L., & Huang, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-87285-0 · Cited by 42
Metadata
- Authors
- Han, K.-H., & Kim, J.-H.
- Venue
- Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512)
- Publisher
- IEEE
- Published
- 2002-11-07
- Volume
- 2
- Pages
- 1354-1360
- OpenAlex
- W2115053914
- Cited by
- 664
- References
- 9
- FWCI
- 9.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Han, K.-H., & Kim, J.-H. (2002). Genetic quantum algorithm and its application to combinatorial optimization problem. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512). https://doi.org/10.1109/cec.2000.870809
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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