Quantum circuits with mixed states
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
A framework for fast quantum mechanical algorithms
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Quantum-inspired immune clonal multiobjective optimization algorithm
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
A novel immune clonal algorithm
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Quantum-Inspired immune clonal algorithm for multiuser detection in DS-CDMA systems
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Associative classification using a bio-inspired algorithm
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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This paper proposes a new immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QICA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. QICA uses a quantum bit, defined as the smallest unit of information, for the probabilistic representation and a quantum bit individual as a string of quantum bits. In QICA, by quantum mutation operator, we can make full use of the information of the current best individual to perform the next search for speeding up the convergence. Information among the subpopulation is exchanged by adopting the quantum crossover operator for improvement of diversity of the population and avoiding prematurity. We execute the proposed algorithm to solve the benchmark problems with 30,100 and 2000 dimensions and very large numbers of local minima. The result shows that the proposed algorithm can close-to-optimal solution by the less computational cost.