An introduction to quantum computing for non-physicists
ACM Computing Surveys (CSUR)
Evolving Quantum Circuits Using Genetic Algorithm
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Quantum optimization for training support vector machines
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Linkage Problem, Distribution Estimation, and Bayesian Networks
Evolutionary Computation
The equation for response to selection and its use for prediction
Evolutionary Computation
Quantum versus evolutionary systems: total versus sampled search
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks
IEEE Transactions on Neural Networks
Model based human motion tracking using probability evolutionary algorithm
Pattern Recognition Letters
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
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The paper proposed a novel quantum-inspired genetic algorithm with only one chromosome, which we called Single-Chromosome Quantum Genetic algorithm (SCQGA). In SCQGA, by bringing the information representation in quantum computing into the algorithm, only one quantum chromosome (QC) is used to represent all possible states of the entire population. A novel quantum evolution method without using conventional genetic operators such as crossover operator and mutation operator is proposed, in which according to the best individuals generated by QC we adjust the quantum probability amplitude with quantum rotation gates so that the QC can produce more promising individuals with higher probability in the next generation. The paper indicated that SCQGA is a new approach belonging to estimation of distribution algorithms (EDAs). Experiments on solving a class of combinatorial optimization problems show that SCQGA performs better than conventional genetic algorithm.