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Cited by 190

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Parallel quantum-inspired genetic algorithm for combinatorial optimization problem

Kuk-Hyun Han, Kui-Hong Park, Ci-Ho Lee, Jong-Hwan Kim·2002·Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)
DOI 10.1109/cec.2001.934358
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Abstract

This paper proposes a new parallel evolutionary algorithm called parallel quantum-inspired genetic algorithm (PQGA). Quantum-inspired genetic algorithm (QGA) 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 the qubit chromosome as a representation, QGA can represent a linear superposition of solutions due to its probabilistic representation. QGA is suitable for parallel structures because of rapid convergence and good global search capability. That is, QGA is able to possess the two characteristics of exploration and exploitation simultaneously. The effectiveness and the applicability of PQGA are demonstrated by experimental results on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that PQGA is superior to QGA as well as other conventional genetic algorithms.

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Metadata

Authors
Han, K.-H., Park, K.-H., Lee, C.-H., & Kim, J.-H.
Venue
Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546)
Publisher
IEEE
Published
2002-11-13
Volume
2
Pages
1422-1429
DOI
10.1109/cec.2001.934358
OpenAlex
W2116400650
Cited by
190
References
0
FWCI
5.71
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Parallel quantum-inspired genetic algorithm for combinatorial optimization problem. https://app.cochat.ai/papers/10.1109/cec.2001.934358
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Han, K.-H., Park, K.-H., Lee, C.-H., & Kim, J.-H. (2002). Parallel quantum-inspired genetic algorithm for combinatorial optimization problem. Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546). https://doi.org/10.1109/cec.2001.934358

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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.