The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Quantum-inspired genetic algorithms
Abstract
A novel evolutionary computing method-quantum inspired genetic algorithms-is introduced, where concepts and principles of quantum mechanics are used to inform and inspire more efficient evolutionary computing methods. The basic terminology of quantum mechanics is introduced before a comparison is made between a classical genetic algorithm and a quantum inspired method for the travelling salesperson problem. It is informally shown that the quantum inspired genetic algorithm performs better than the classical counterpart for a small domain. The paper concludes with some speculative comments concerning the relationship between quantum inspired genetic algorithms and various complexity classes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A method for obtaining digital signatures and public-key cryptosystems
Rivest, R. L., Shamir, A., & Adleman, L. (1978) · Communications of the ACM · doi:10.1145/359340.359342 · Cited by 13,462
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
Simulating physics with computers
Feynman, R. P. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf02650179 · Cited by 7,691
Feynman, R. P. (1986) · Foundations of Physics · doi:10.1007/bf01886518 · Cited by 1,770
Quantum computational networks
Deutsch, D. E. (1989) · Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences · doi:10.1098/rspa.1989.0099 · Cited by 1,270
Lloyd, S. (1995) · Scientific American · doi:10.1038/scientificamerican1095-140 · Cited by 150
Susskind, R. E. (2019) · doi:10.1093/oso/9780198838364.003.0031 · Cited by 26
Cited by
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
Metaheuristic research: a comprehensive survey
Hussain, K., Mohd Salleh, M. N., Cheng, S., & Shi, Y. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-017-9605-z · Cited by 864
Genetic quantum algorithm and its application to combinatorial optimization problem
Han, K.-H., & Kim, J.-H. (2002) · Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512) · doi:10.1109/cec.2000.870809 · Cited by 664
Heap-based optimizer inspired by corporate rank hierarchy for global optimization
Askari, Q., Saeed, M., & Younas, I. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113702 · Cited by 380
Quantum-inspired evolutionary algorithms: a survey and empirical study
Zhang, G. (2011) · Journal of Heuristics · doi:10.1007/s10732-010-9136-0 · Cited by 295
Metadata
- Authors
- Narayanan, A., & Moore, M.
- Venue
- Proceedings of IEEE International Conference on Evolutionary Computation
- Publisher
- IEEE
- Published
- 2002-12-24
- Pages
- 61-66
- OpenAlex
- W1962752774
- Cited by
- 649
- References
- 7
- FWCI
- 9.24
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Quantum-inspired genetic algorithms. https://app.cochat.ai/papers/10.1109/icec.1996.542334
- The paper
- Narayanan, A., & Moore, M. (2002). Quantum-inspired genetic algorithms. Proceedings of IEEE International Conference on Evolutionary Computation. https://doi.org/10.1109/icec.1996.542334
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
