Abstract
This paper proposes a novel type of quantum-inspired evolutionary algorithm (QiEA) for numerical optimization inspired by the multiple universes principle of quantum computing, which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Numerical optimization problems are an important field of research with several applications in several areas: industrial plant optimization, data mining and many others, and although being successfully used for solving several optimization problems, evolutionary algorithms still present issues that can reduce their performances when faced with task where the evaluation function is computationally intensive. In order to address those issues the QiEA represent the most recent advance in the field of evolutionary computation. This work present some application about combinatorial and numerical optimization problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Deutsch, D.: Quantum Theory, the Church-Turing principle and the universal quantum computer. Pro.of the Royal Society of London A 400, 97–117 (1985)
Benioff, P.: The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines. Journal of Statistical Physics 22, 563–591 (1980)
Deutsch, D., Jozsa, R.: Rapid solution of problems by quantum computation. Pro.of the Royal Society of London A 439, 553–558 (1992)
Simon, D.R.: On the Power of Quantum Computation. In: Proc. of the 35th Annual Symposium on Foundations of Computer Science, pp. 116–123. IEEE Press, Piscataway (1994)
Shor, P.W.: Algorithms for Quantum Computation: Discrete Logarithms and Factoring. In: Proc. of the 35th Annual Symposium on Foundations of Computer Science, pp. 124–134. IEEE Press, Piscataway (1994)
Grover, L.K.: Quantum Mechanical Searching. In: Proc. of the 1999 Congress on Evolutionary Computation, vol. 3, pp. 2255–2261. IEEE Press, Piscataway (1999)
Lee, S.-C.: Quantum Computation. Technical report, Department of Physics, Korea Advanced Institute of Science and Technology, Korea
Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach, 2nd edn. Springer, London (2007)
Eshelman, L.J.: Genetic Algorithms. In: Back, T., Fogel, D.B., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation, pp. B1.2:1–B1.2:11. OUP, New York (1997)
Porto, V.W.: Evolutionary Programming. In: Back, T., Fogel, D.B., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation, pp. B1.4:1–B1.4:10. OUP, New York (1997)
Han, K.-H., Kim, J.-H.: Quantum-inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Trans. on Evolutionary Computation 6(6), 580–593 (2002)
Hirvensalo, M.: Quantum computing. Springer, Heidelberg (2004)
Han, K.-H.: andKim, J.-H.: Analysis of Quantum-inspired Evolutionary Algorithm. In: Proc. of the 2001 Int. Conf. on Artificial Intelligence, vol. 2, pp. 727–730. CSREA Press (2001)
Defoin-Platel, M., Schliebs, S., Kasabov, N.: Quantum-Inspired Evolutionary Algorithm: A Multimodel EDA. IEEE Transactions on Evolutionary Computation 13(6), 1218–1232 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fiasché, M. (2012). A Quantum-Inspired Evolutionary Algorithm for Optimization Numerical Problems. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_83
Download citation
DOI: https://doi.org/10.1007/978-3-642-34487-9_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34486-2
Online ISBN: 978-3-642-34487-9
eBook Packages: Computer ScienceComputer Science (R0)