A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Quantum computation and quantum information
Quantum computation and quantum information
Quantum-Behaved Particle Swarm Optimization with Mutation Operator
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
A novel immune evolutionary algorithm incorporating chaos optimization
Pattern Recognition Letters
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
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A quantum novel genetic algorithm based on subpopulation parallel computing is presented, where quantum coding and rotation angle are improved to inspire more efficient genetic computing methods. In the algorithm, each axis of the solution space is divided into k parts, the individual (or chromosome) from each different subspace being coded differently, and the probability amplitude of each quantum bit or Q-bit is regarded as two paratactic genes. The basic quantum computing theory and classical quantum genetic algorithm are briefly introduced before a novel algorithm is presented for the function optimum and PID problem. Through a comparison between the novel algorithm and the classical counterpart, it is shown that the quantum inspired genetic algorithm performs better on running speed and optimization capability.