The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
Abstract
This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.
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Cites
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An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems
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Heap-based optimizer inspired by corporate rank hierarchy for global optimization
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Quantum-inspired evolutionary algorithms: a survey and empirical study
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Quantum-inspired metaheuristic algorithms: comprehensive survey and classification
Gharehchopogh, F. S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10280-8 · Cited by 253
Metadata
- Authors
- Han, K.-H., & Kim, J.-H.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2002-12
- Volume
- 6
- Issue
- 6
- Pages
- 580-593
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2122274805
- Cited by
- 1,585
- References
- 20
- FWCI
- 10.71
- Type
- journal article
- Language
- en
- Open access
- closed
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