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Quantum-inspired evolutionary algorithms: a survey and empirical study

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Abstract

Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

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Abbreviations

QIEA:

Quantum-inspired evolutionary algorithm

EDQA:

Evolutionary-designed quantum algorithm

Q-bit:

Quantum-inspired bit

EA:

Evolutionary algorithm

GA:

Genetic algorithm

Q-gate:

Quantum-inspired gate

QEA:

Quantum evolutionary algorithm

bQIEAcm:

bQIEA with crossover and mutation operators

bQIEAn:

bQIEA with a novel update method for Q-gates

bQIEAi:

Hybrid algorithm of bQIEA and immune algorithms

DS-CDMA:

Directed-sequence code-division multiple access

bQIEApso:

Hybrid algorithm of bQIEA and PSO

bQIEAcga:

Hybrid algorithm of bQIEA and CGA

bQIEAo:

Original version of bQIEA

bQIEAh:

Hybrid bQIEA

rQIEA:

Real observation QIEA

EDA:

Estimation of distribution algorithm

bQIEA:

Binary observation QIEA

OMUD:

Optimal multiuser detector

PGA:

Polyploid GA

PSO:

Particle swarm optimization

MFD:

Matched filter detector

CGA:

Conventional GA

iQIEA:

QIEA-like algorithm

References

  • Abdesslem, L., Soham, M., Mohamed, B.: Multiple sequence alignment by quantum genetic algorithm. In: Proc. IPDPS, pp. 360–367 (2006)

  • Abs da Cruz, A., Hall Barbosa, C., Pacheco, M., Vellasco, M.: Quantum-inspired evolutionary algorithms and its application to numerical optimization problems. Lect. Not. Comput. Sci. 3316, 212–217 (2004)

    Article  Google Scholar 

  • Abs da Cruz, A., Pacheco, M., Vellasco, M., Barbosa, C.: Cultural operators for a quantum-inspired evolutionary algorithm applied to numerical optimization problems. Lect. Not. Comput. Sci. 3562, 1–10 (2005)

    Article  Google Scholar 

  • Abs da Cruz, A., Vellasco, M., Pacheco, M.: Quantum-inspired evolutionary algorithm for numerical optimization. In: Proc. CEC, pp. 2630–2637 (2006)

  • Abs da Cruz, A., Vellasco, M., Pacheco, M.: Quantum-inspired evolutionary algorithm for numerical optimization. In: Studies in Computational Intelligence, vol. 75, pp. 19–37 (2007)

  • Akbarzadeh-T, M.: Evolutionary quantum algorithms for structural design. In: Proc. IEEE SMC vol. 4, pp. 3077–3082 (2005)

  • Al-Othman, A., Al-Fares, F., EL-Nagger, K.: Power system security constrained economic dispatch using real coded quantum inspired evolution algorithm. Int. J. Electr. Comput. Syst. Eng 1(4), 199–206 (2007)

    Google Scholar 

  • Alfares, F., Esat, I.: Real-coded quantum inspired evolution algorithm applied to engineering optimization problems. In: Proc. ISoLA, pp. 169–176 (2006)

  • Alfares, F., Alfares, M., Esat, I.: Quantum-inspired evolution algorithm: experimental analysis. In: Proc. ACDM, pp. 377–389 (2004)

  • Araujo, M., Nedjah, N., Mourelle, L.: Quantum-inspired evolutionary state assignment for synchronous finite state machines. J. Univers. Comput. Sci. 14(15), 2532–2548 (2008)

    Google Scholar 

  • Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: Proc. CEC, pp. 1769–1776 (2005)

  • Bäck, T., Hammel, U., Schwefel, H.: Evolutionary computation: comments on the history and current state. IEEE Trans. Evol. Comput. 1(2), 3–17 (1997)

    Article  Google Scholar 

  • Baluja, S.: Population based incremental learning: a method for integrating genetic search based function optimization and competitive learning. Technical Report No. CMU-CS-94-163. Carnegie Mellon University, Pittsburgh, Pennsylvania (1994)

  • Baluja, S., Davies, S.: Using optimal dependency trees for combinatorial optimization: Learning the structure of search space. Technical Report CMU-CS-97-107. Carnegie Mellon University, Pittsburgh, Pennsylvania (1997)

  • Barenco, A., Bennett, C., Cleve, R., Divincenzo, D., Margolus, N., Shor, P., Sleator, T., Smolin, J., Weinfurter, H.: Elementary gates for quantum computation. Phys. Rev. A 52(5), 3457–3467 (1995)

    Article  Google Scholar 

  • Bennett, C., DiVincenzo, D.: Quantum information and computation. Nature 404, 247–255 (2000)

    Article  Google Scholar 

  • Bi, X., Jin, G.: Image segmentation algorithm based on quantum immune programming. In: Proc IEEE ICIT, pp. 403–407 (2007)

  • Box, G.: Evolutionary operation: a method for increasing industrial productivity. Appl. Stat. 6, 81–101 (1957)

    Article  Google Scholar 

  • Bremermann, H.: Optimization through evolution and recombination. In: Yovits MC (ed) Self-Organizing Systems, Spartan, Washington DC (1962)

  • Burian, R.: Underappreciated pathways toward molecular genetics as illustrated by Jean Brachet’s cytochemical embryology. In: Sarkar, S. (ed.) The Philosophy and History of Molecular Biology: New Perspectives, pp. 67–85. Kluwer, Dordrecht (1996)

    Google Scholar 

  • Chaiyaratana, N., Piroonratana, T., Sangkawelert, N.: Effects of diversity control in single objective and multi-objective genetic algorithms. J. Heuristics 13(1), 1–34 (2007)

    Article  Google Scholar 

  • Chen, H., Zhang, J., Zhang, C.: Chaos updating rotated gates quantum-inspired genetic algorithm. In: Proc. ICCCAS, pp. 1108–1112 (2004)

  • Collingwood, E., Corne, D., Ross, P.: Useful diversity via multiploidy. In: Proc. CEC, pp. 810–813 (1996)

  • Corne, D., Collingwood, E., Ross, P.: Investigating multiploidy’s niche. Lect. Not. Comput. Sci. 1143, 189–198 (1996)

    Article  Google Scholar 

  • Darwin, C.: On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. Murray, London (1859)

    Google Scholar 

  • De Bonet, J., Isbell, C., Viola, P.: Mimic: Finding optima by estimating probability densities. In: NIPS. MIT Press, Cambridge (1997)

    Google Scholar 

  • Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 10, 371–395 (2002)

    Article  Google Scholar 

  • Ding, S., Jin, Z., Yang, Q.: Evolving quantum circuits at the gate level with a hybrid quantum-inspired evolutionary algorithm. Soft Comput. 12(11), 1059–1072 (2008)

    Article  MATH  Google Scholar 

  • DiVincenzo, D.: Quantum gates and circuits. Proc. R. Soc. A, Math. Phys. Eng. Sci. 454, 261–276 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  • Du, J., Tian, Y., Zuo, M., Zhou, Y.: Using quantum immune clone algorithm in the prediction of tourism emergency events. In: Proc. ICCAS, pp. 2519–2522 (2007)

  • Eddy, S.: Infernal: inference of RNA alignments. http://www.fli-leibniz.de/RNA.html, the RNA World Website (2009)

  • Eshelman, L.: The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In: Rawlin, S.M.G.J.E. (ed.) Foundations of Genetic Algorithms, pp. 265–283. Morgan Kaufmann, San Mateo (1991)

    Google Scholar 

  • Fan, K., Brabazon, A., O’Sullivan, C., O’Neill, M.: Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm. In: Proc. GECCO, pp. 1983–1990 (2007)

  • Feng, X., Wang, Y., Ge, H., Zhou, C., Liang, Y.: Quantum-inspired evolutionary algorithm for travelling salesman problem. Comput. Methods, 1363–1367 (2006)

  • Fogel, L., Owens, A., Walsh, M.: Artificial Intelligence Through Simulated Evolution. Wiley, New York (1966)

    MATH  Google Scholar 

  • Fraser, A.: Simulation of genetic systems by automatic digital computers. Aust. J. Biol. Sci. 10, 484–491 (1957)

    Google Scholar 

  • Friedberg, R.: A learning machine: Part i. IBM J. Res. Dev. 2, 2–13 (1958)

    Article  MathSciNet  Google Scholar 

  • Friedberg, R., Dunham, B., North, J.: A learning machine: Part ii. IBM J. Res. Dev. 3, 282–287 (1959)

    Article  MathSciNet  Google Scholar 

  • Ganesh, V., Singhal, G.: Quantum-inspired evolutionary algorithms and binary particle swarm optimization for training MLP and SRN neural networks. J. Comput. Theor. Nanosci. 2(4), 561–568 (2005)

    Article  Google Scholar 

  • Gao, H., Xu, G., Wang, Z.: A novel quantum evolutionary algorithm and its application. In: Proc. WCICA, pp. 3638–3642 (2006)

  • Garcia, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J. Heuristics 15(6), 617–644 (2009)

    Article  MATH  Google Scholar 

  • Gardner, P., Wilm, A., Washietl, S.: A benchmark of multiple sequence alignment programs upon structural RNAs. Nucleic Acids Res. 33, 2433–2439 (2005)

    Article  Google Scholar 

  • Glassner, A.: Quantum computing, part 2. IEEE Comput. Graph. Appl. 86–95 (2001a)

  • Glassner, A.: Quantum computing, part 3. IEEE Comput. Graph. Appl. 73–82 (2001b)

  • Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley/Longman, Boston (1989)

    MATH  Google Scholar 

  • Grigorenko, I., Garcia, M.: Ground-state wave functions of two-particle systems determined using quantum genetic algorithms. Physica A, Stat. Mech. Its Appl. 291(1–4), 439–448 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  • Grigorenko, I., Garcia, M.: Calculation of the partition function using quantum genetic algorithms. Physica A, Stat. Mech. Its Appl. 313(3–4), 463–470 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  • Grover, L.: Quantum mechanics helps in searching for a needle in a haystack. Phys. Rev. Lett. 79(2), 325–328 (1997)

    Article  Google Scholar 

  • Grover, L.: Quantum computation. In: Proc. VLSI Design, pp. 548–553 (1999)

  • Gu, J., Gu, M., Cao, C., Gu, X.: A novel competitive co-evolutionary quantum genetic algorithm for stochastic job shop scheduling problem. Comput. Oper. Res. 37(5), 927–937 (2009a)

    Article  MathSciNet  Google Scholar 

  • Gu, J., Gu, X., Gu, M.: A novel parallel quantum genetic algorithm for stochastic job shop scheduling. J. Math. Anal. Appl. 355(1), 63–81 (2009b)

    Article  MathSciNet  MATH  Google Scholar 

  • Guo, R., Li, B., Zou, Y., Zhuang, Z.: Hybrid quantum probabilistic coding genetic algorithm for large scale hardware-software co-synthesis of embedded systems. In: Proc. CEC, pp. 3454–3458 (2007)

  • Han, K., Kim, J.: Genetic quantum algorithm and its application to combinatorial optimization problem. In: Proc. CEC, vol. 2, pp. 1354–1360 (2000)

  • Han, K., Kim, J.: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6(6), 580–593 (2002)

    Article  Google Scholar 

  • Han, K., Kim, J.: On setting the parameters of QEA for practical applications: Some guidelines based on empirical evidence. Lect. Not. Comput. Sci. 2723, 427–428 (2003a)

    Article  Google Scholar 

  • Han, K., Kim, J.: On setting the parameters of quantum-inspired evolutionary algorithm for practical application. In: Proc. CEC, pp. 178–184 (2003b)

  • Han, K., Kim, J.: Quantum-inspired evolutionary algorithms with a new termination criterion, h-epsilon gate, and two-phase scheme. IEEE Trans. Evol. Comput. 8(2), 156–169 (2004)

    Article  Google Scholar 

  • Han, K., Kim, J.: On the analysis of the quantum-inspired evolutionary algorithm with a single individual. In: Proc. CEC, pp. 2622–2629 (2006)

  • Han, K., Park, K., Lee, C., Kim, J.: Parallel quantum-inspired genetic algorithm for combinatorial optimization problem. In: Proc. CEC, vol. 2, pp. 1422–1429 (2001)

  • Harik, G.: Linkage learning via probabilistic modeling in the ECGA. Tech. Rep., Illinois Genetic Algorithm Laboratory, University of Illinois, Urbana, Illinois (1999)

  • Harik, G.R., Lobo, F.G., Goldberg, DE: The compact genetic algorithm. In: Proc. EC, pp. 523–528 (1998)

  • Herrera, F., Lozano, M.: Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In: Genetic Algorithms and Soft Comput., pp. 95–125. Physica-Verlag, Heidelberg (1996)

    Google Scholar 

  • Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms: operators and tools for the behavioral analysis. Artif. Intell. Rev. 12(4), 265–319 (1998)

    Article  MATH  Google Scholar 

  • Herrera, F., Lozano, M., Sanchez, A.M.: A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. Int. J. Intell. Syst. 18(3), 309–338 (2003)

    Article  MATH  Google Scholar 

  • Hey, T.: Quantum computing: an introduction. Comput. Control Eng. J. 10(3), 105–112 (1999)

    Article  Google Scholar 

  • Hinterding, R.: Representation, constraint satisfaction and the knapsack problem. In: Proc. CEC, pp. 1286–1292 (1999)

  • Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  • Huang, Y., Tang, C., Wang, S.: Quantum-inspired swarm evolution algorithm. In: Proc. CISW, pp. 208–211 (2007)

  • Huo, H., Stojkovic, V.: Two-phase quantum based evolutionary algorithm for multiple sequence alignments. In: Proc. ICCIAS, pp. 374–379 (2006)

  • Huo, H., Stojkovic, V.: Two-phase quantum based evolutionary algorithm for multiple sequence alignment. In: Lecture Notes in Artificial Intelligence, vol. 4456, pp. 11–21 (2007)

  • Imabeppu, T., Nakayama, S., Ono, S.: A study on a quantum-inspired evolutionary algorithm based on pair swap. Artif. Life Robot. 12(1), 148–152 (2008)

    Article  Google Scholar 

  • Jang, J.S., Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm-based face verification. In: Lecture Notes in Computer Science, vol. 2724, pp. 2147–2156 (2003)

  • Jang, J.S., Han, K.H., Kim, J.H.: Evolutionary algorithm-based face verification. Pattern Recognit. Lett. 25(16), 1857–1865 (2004a)

    Article  Google Scholar 

  • Jang, J.S., Han, K.H., Kim, J.H.: Face detection using quantum-inspired evolutionary algorithm. In: Proc. CEC, pp. 2100–2106 (2004b)

  • Jang, S.H., Jung, Y.W., Kim, W., Shin, J.R., Park, J.B.: A thermal unit commitment approach based on a bounded quantum evolutionary algorithm. Trans. Korean Inst. Electr. Eng. 58(6), 1057–1064 (2009)

    Google Scholar 

  • Jeong, Y.W., Park, J.B., Shin, J.R., Lee, K.Y.: A thermal unit commitment approach using an improved quantum evolutionary algorithm. Electr. Power Compon. Syst. 37(7), 770–786 (2009)

    Article  Google Scholar 

  • Jiao, L., Li, Y.: Quantum-inspired immune clonal optimization. In: Proc. ICNN&B, pp. 461–468 (2005)

  • Jiao, L., Li, Y., Gong, M., Zhang, X.: Quantum-inspired immune clonal algorithm for global optimization. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 38(5), 1234–1253 (2008)

    Article  Google Scholar 

  • John, V., John, P.: Reactive power and voltage control based on general quantum genetic algorithms. Expert Syst. Appl. 36(3), 6118–6126 (2009)

    Article  Google Scholar 

  • Kent, A., Williams, J.G.: Encyclopedia of Computer Science and Technology. CRC Press, Boca Raton (1999)

    Google Scholar 

  • Khorsand, A.: Quantum gate optimization in a meta-level genetic quantum algorithm. In: Proc. IEEE SMC, pp. 3055–3062 (2005)

  • Khorsand, A.: Genetic quantum algorithm for voltage and pattern design of piezoelectric actuator. In: Proc. CEC, pp. 2593–2600 (2006)

  • Kim, K.H., Hwang, J.Y., Han, K.H., Kim, J.H., Park, K.H.: A quantum-inspired evolutionary computing algorithm for disk allocation method. IEICE Trans. Inf. Syst. E86D(3), 645–649 (2003)

    Google Scholar 

  • Kim, Y., Kim, J.H., Han, K.H.: Quantum-inspired multiobjective evolutionary algorithm for multiobjective 0/1 knapsack problems. In: Proc. CEC, pp. 2601–2606 (2006)

  • Koumousis, V.K., Katsaras, C.P.: A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans. Evol. Comput. 10(1), 19–27 (2006)

    Article  Google Scholar 

  • Koza, J.R., Al-Sakran, S.H., Jones, L.W.: Cross-domain features of runs of genetic programming used to evolve designs for analog circuits, optical lens systems, controllers, antennas, mechanical systems, and quantum computing circuits. In: Proc NASA/DoD EH, pp. 205–212 (2005)

  • Larran̆aga, P., Etxeberria, R., Lozano, J.A., Peña, J.M.: Combinatorial optimization by learning and simulation of bayesian networks. In: Proc. UAI, pp. 343–352 (2000)

  • Lau, T.W., Chung, C.Y., Wong, K.P., Chung, T.S., Ho, S.L.: Quantum-inspired evolutionary algorithm approach for unit commitment. IEEE Trans. Power Syst. 24(3), 1503–1512 (2009)

    Article  Google Scholar 

  • Li, B., Zhuang, Z.: Genetic algorithm based-on the quantum probability representation. In: Lecture Notes in Computer Science, vol. 2412, pp. 79–95 (2002)

  • Li, B.B., Wang, L.: A hybrid quantum-inspired genetic algorithm for multi-objective scheduling. In: Lecture Notes in Computer Science, vol. 4113, pp. 511–522 (2006)

  • Li, B.B., Wang, L.: A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 37(3), 576–591 (2007)

    Article  Google Scholar 

  • Li, N., Du, P., Zhao, H.J.: Independent component analysis based on improved quantum genetic algorithm: Application in hyperspectral images. In: Proc. IGARSS, pp. 4323–4326 (2005a)

  • Li, P., Li, S.: Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits. Neurocomputing 72(1–3), 581–591 (2008)

    Article  Google Scholar 

  • Li, Y., Jiao, L.: Quantum-inspired immune clonal algorithm. In: Lecture Notes in Computer Science, vol. 3627, pp. 304–317 (2005)

  • Li, Y., Jiao, L.: Quantum-inspired immune clonal multiobjective optimization algorithm. In: Lecture Notes in Artificial Intelligence, vol. 4426, pp. 672–679 (2007)

  • Li, Y., Liu, F.: A novel immune clonal algorithm. In: Lecture Notes in Computer Science, vol. 4222, pp. 31–40 (2006)

  • Li, Y., Jiao, L., Liu, F.: Self-adaptive chaos quantum clonal evolutionary programming. In: Proc. ICSP, vol. 2, pp. 1550–1553 (2004a)

  • Li, Y., Zhang, Y.N., Zhao, R.C., Jiao, L.C.: An edge detector based on parallel quantum-inspired evolutionary algorithm. In: Proc. ICMLC, pp. 4062–4066

  • Li, Y., Zhang, Y.N., Zhao, R.C., Jiao, L.C.: The immune quantum-inspired evolutionary algorithm. In: Proc. IEEE ICSMC, pp. 3301–3305 (2004c)

  • Li, Y., Zhang, Y., Cheng, Y., Jiang, X., Zhao, R.: A novel immune quantum-inspired genetic algorithm. In: Lecture Notes in Computer Science, vol. 3612, pp. 215–218 (2005b)

  • Li, Y., Jiao, L., Gou, S.: Quantum-inspired immune clonal algorithm for multiuser detection in DS-CDMA systems. In: Lecture Notes in Computer Science, vol. 4247, pp. 80–87 (2006)

  • Li, Y.Y., Jiao, L.C.: Quantum-inspired immune clonal algorithm and its application. In: Proc. ISPACS, pp. 670–673 (2008)

  • Li, Z., Rudolph, G., Li, K.: Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms. Comput. Math. Appl. 57(11–12), 1843–1854 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  • Liu, F., Li, S.Q., Liang, M., Hu, L.Z.: Wideband signal DOA estimation based on modified quantum genetic algorithm. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E89A(3), 648–653 (2006)

    Article  Google Scholar 

  • Liu, H., Zhang, D., Yan, J.Q., Li, Z.S.: Fast and robust portrait segmentation using QEA and histogram peak distribution methods. In: Lecture Notes in Computer Science, vol. 3645, pp. 920–928 (2005)

  • Liu, H., Zhang, G., Liu, C., Fang, C.: A novel memetic algorithm based on real-observation quantum-inspired evolutionary algorithms. In: Proc. ISKE, pp. 486–490 (2008)

  • Lozano, M., Herrera, F., Krasnogor, N., Molina, D.: Real-coded memetic algorithms with crossover hill-climbing. Evol. Comput. 12(3), 273–302 (2004)

    Article  Google Scholar 

  • Lozano, M., Herrera, F., Cano, J.R.: Replacement strategies to maintain useful diversity in steady-state genetic algorithms. In: Soft Computing: Methodology and Applications. Springer, Berlin (2005)

    Google Scholar 

  • Lozano, M., Herrera, F., Cano, J.R.: Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Inf. Sci. 178(23), 4421–4433 (2008)

    Article  Google Scholar 

  • Lu, T.C., Juang, J.C., Yu, G.R.: On-line outliers detection by neural network with quantum evolutionary algorithm. In: Proc ICICIC, pp. 254–257 (2008)

  • Luo, Z., Wang, P., Li, Y., Zhang, W., Tang, W., Xiang, M.: Quantum-inspired evolutionary tuning of SVM parameters. Progr. Nat. Sci. 18(4), 475–480 (2008)

    Article  MathSciNet  Google Scholar 

  • Lv, Y.J., Liu, N.X.: Application of quantum genetic algorithm on finding minimal reduct. In: Proc. GrC, pp. 728–733 (2007)

  • Malossini, A., Blanzieri, E., Calarco, T.: QGA: a quantum genetic algorithm. Technical Report No. DIT-04-105, Informatica e Telecommunicazioni, University of Trento (2004)

  • Malossini, A., Blanzieri, E., Calarco, T.: Quantum genetic optimization. IEEE Trans. Evol. Comput. 12(2), 231–241 (2008)

    Article  Google Scholar 

  • Martinez, A., Benavente, R.: The AR face database. http://rvl1.ecn.purdue.edu/~aleix/aleixfaceDB.html (1998)

  • Meshoul, S., Layeb, A., Batouche, M.: A quantum evolutionary algorithm for effective multiple sequence alignment. In: Lecture Notes in Artificial Intelligence, vol. 3808, pp. 260–271 (2005a)

  • Meshoul, S., Mahdi, K., Batouche, M.: A quantum inspired evolutionary framework for multi-objective optimization. In: Lecture Notes in Artificial Intelligence, vol. 3808, pp. 190–201 (2005b)

  • Moore, M., Narayanan, A.: Quantum-inspired computing. Technical Report, Department of Computer Science, University Exeter, Exeter, UK (1995)

  • Mühlenbein, H., Mahnig, T.: The equation for response to selection and its use for prediction. Evol. Comput. 5(3), 303–346 (1998)

    Article  Google Scholar 

  • Mühlenbein, H., Mahnig, T.: The factorized distribution algorithm for additively decomposed functions. In: Proc. CEC, pp. 752–759 (1999)

  • Narayanan, A.: Quantum computing for beginners. In: Proc. CEC, pp. 2231–2238 (1999)

  • Narayanan, A., Moore, M.: Quantum-inspired genetic algorithms. In: Proc. CEC, pp. 61–66 (1996)

  • Nielsen, A.M., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2000)

    MATH  Google Scholar 

  • Niu, Q., Zhou, T., Ma, S.: A quantum-inspired immune algorithm for hybrid flow shop with makespan criterion. J. Univers. Comput. Sci. 15(4), 765–785 (2009)

    MathSciNet  Google Scholar 

  • Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008)

    Article  Google Scholar 

  • Notredame, C., Holm, L., Higgins, D.: Coffee: an objective functions for multiple sequence alignments. Bioinformatics 14, 407–422 (1998)

    Article  Google Scholar 

  • Pan, G.F., Xia, K.W., Dong, Y., Shi, J.: An improved LS-SVM based on quantum PSO algorithm and its application. In: Proc. ICNC, pp. 606–610 (2007)

  • Pelikan, M., Mühlenbein, H.: The bivariate marginal distribution algorithm. In: Advances in Soft Computing—Engineering Design and Manufacturing, pp. 521–535 (1999)

  • Pelikan, M., Goldberg, D., Cantú-paz, E.: Linkage problem, distribution estimation and bayesian networks. Evol. Comput. 8(3), 311–340 (2000)

    Article  Google Scholar 

  • Pelikan, M., Goldberg, D., Lobo, F.G.: A survey of optimization by building and using probabilistic models. Comput. Optim. Appl. 21, 5–20 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  • Platel, M., Schliebs, S., Kasabov, N.: Quantum-inspired evolutionary algorithm: A multimodel EDA. IEEE Trans. Evol. Comput. 13(6), 1218–1232 (2009)

    Article  Google Scholar 

  • Platelt, M.D., Schliebs, S., Kasabov, N.: A versatile quantum-inspired evolutionary algorithm. In: Proc. CEC, pp. 423–430 (2007)

  • Pötz, W., Fabian, J. (eds.) Quantum Coherence: from Quarks to Solids. Springer, Berlin (2006)

    MATH  Google Scholar 

  • Price, K., Storn, R.M., Lampinen, JA: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005)

    MATH  Google Scholar 

  • Qin, C., Zheng, J., Lai, J.: A multiagent quantum evolutionary algorithm for global numerical optimization. In: LNBI, vol. 4689, pp. 380–389 (2007)

  • Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systemenach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart (1973)

    Google Scholar 

  • Ruiz, F.: MCDM numerical instances library. http://www.univ-valencienne.fr/ROAD/MCDM, international Society on Multiple Criteria Decision Making (2009)

  • Rylander, B., Soule, T., Foster, J., Alves-Foss, J.: Quantum genetic algorithms. In: Proc. GECCO, pp. 373–377 (2000)

  • Sahin, M., Tomak, M.: The self-consistent calculation of a spherical quantum dot: a quantum genetic algorithm study. Physica E, Low-Dimens. Syst. Nanostruct. 28(3), 247–256 (2005)

    Article  Google Scholar 

  • Sahin, M., Atav, U., Tomak, M.: Quantum genetic algorithm method in self-consistent electronic structure calculations of a quantum dot with many electrons. Int. J. Mod. Phys. C 16(9), 1379–1393 (2005)

    Article  MATH  Google Scholar 

  • Sailesh Babu, G.S., Bhagwan Das, D., Patvardhan, C.: Real-parameter quantum evolutionary algorithm for economic load dispatch. IET Gener. Transm. Distrib. 2(1), 22–31 (2008)

    Article  Google Scholar 

  • Santana, R., Lozano, J., Larrañaga, P.: Protein folding in simplified models with estimation of distribution algorithms. IEEE Trans. Evol. Comput. 12(4), 418–438 (2008)

    Article  Google Scholar 

  • Schwefel, H.P.: Evolutionsstrategie und numerische optimierung. PhD dissertation, Technische Berlin, Germany (1975)

  • Shor, P.W.: Algorithms for quantum computation: discrete logarithms and factoring. In: Proc. SFCS, pp. 124–134 (1994)

  • Shu, W.N.: Optimal resource allocation on grid computing using a quantum chromosomes genetic algorithm. In: Proc. DMAMH, pp. 254–257 (2007)

  • Shu, W.N., He, B.J.: A quantum genetic simulated annealing algorithm for task scheduling. In: Lecture Notes in Computer Science, vol. 4683, pp. 169–176 (2007)

  • Sofge, D.A.: Toward a framework for quantum evolutionary computation. In: Proc. CIS, pp. 789–794 (2006)

  • Spector, L., Barnum, H., Bernstein, H.: Genetic programming for quantum computers. In: Proc. GP, pp. 365–373 (1998)

  • Spector, L., Barnum, H., Bernstein, H., Swamy, J.N.: Finding a better-than-classical quantum and/or algorithm using genetic programming. In: Proc. CEC, pp. 2239–2246 (1999)

  • Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey. Computer 27, 17–26 (1994)

    Article  Google Scholar 

  • Su, H., Yang, Y., Zhao, L.: Classification rule discovery with DE QDE algorithm. Expert Syst. Appl. 37(2), 1216–1222 (2010)

    Article  Google Scholar 

  • Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University (2005)

  • Talbi, H., Batouche, M., Draa, A.: A quantum-inspired genetic algorithm for multi-source affine image registration. In: Lecture Notes in Computer Science, vol. 3211, pp. 147–154 (2004a)

  • Talbi, H., Draa, A., Batouche, M.: A new quantum-inspired genetic algorithm for solving the travelling salesman problem. In: Proc ICIT, pp. 1192–1197 (2004b)

  • Talbi, H., Draa, A., Batouche, M.C.: A genetic quantum algorithm for image registration. In: Proc. ICTTA, pp. 395–396 (2004c)

  • Thompson, J.D., Plewniak, F., Poch, O.: Balibase: A benchmark alignment database for the evaluation of multiple alignment programs. Bioinformatics 15, 87–88 (1999)

    Article  Google Scholar 

  • Udrescu, M., Prodan, L., Vladutiu, M.: Implementing quantum genetic algorithms: a solution based on Grover’s algorithm. In: Proc. CF, pp. 14–16 (2006)

  • Vlachoglannis, J.G.: Quantum-inspired evolutionary algorithm for real and reactive power dispatch. IEEE Trans. Power Syst. 23(4), 1627–1636 (2008)

    Article  Google Scholar 

  • Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1, 337–348 (1994)

    Article  Google Scholar 

  • Wang, L., Li, L.P.: An effective hybrid quantum-inspired evolutionary algorithm for parameter estimation of chaotic systems. Expert Syst. Appl. 37(2), 1279–1285 (2010)

    Article  Google Scholar 

  • Wang, L., Tang, F., Wu, H.: Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation. Appl. Math. Comput. 171(2), 1141–1156 (2005a)

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, L., Wu, H., Tang, F., Zheng, D.Z.: A hybrid quantum-inspired genetic algorithm for flow shop scheduling. In: Lecture Notes in Computer Science, vol. 3645, pp. 636–644 (2005b)

  • Wang, L., Wu, H., Zheng, D.Z.: A quantum-inspired genetic algorithm for scheduling problems. In: Lecture Notes in Computer Science, vol. 3612, pp. 417–423 (2005c)

  • Wang, L., Niu, Q., Fei, M.R.: A novel quantum ant colony optimization algorithm. In: Lecture Notes in Computer Science, vol. 4688, pp. 277–286 (2007a)

  • Wang, X.H., Ying, Y., Xiao, J.M.: Application of quantum genetic algorithm in logistics distribution planning. In: Proc. CCC, pp. 759–762 (2007b)

  • Wang, Y., Feng, X.Y., Huang, Y.X., Zhou, W.G., Liang, Y.C., Zhou, C.G.: A novel quantum swarm evolutionary algorithm for solving 0-1 knapsack problem. In: Lecture Notes in Computer Science, vol. 3611, pp. 698–704 (2005d)

  • Wang, Y., Feng, X.Y., Huang, Y.X., Pu, D.B., Zhou, W.G., Liang, Y.C., Zhou, C.G.: A novel quantum swarm evolutionary algorithm and its applications. Neurocomputing 70(4–6), 633–640 (2007c)

    Google Scholar 

  • Wei, W., Li, B., Zou, Y., Zhang, W., Zhuang, Z.: A multi-objective HW-SW co-synthesis algorithm based on quantum-inspired evolutionary algorithm. Int. J. Comput. Intell. Appl. 7(2), 129–148 (2008)

    Article  MATH  Google Scholar 

  • Whitley, D., Rana, S., Dzubera, J., Mathias, E.: Evaluating evolutionary algorithms. Artif. Intell. Rev. 85, 245–276 (1996)

    Article  Google Scholar 

  • Wu, Q., Jiao, L., Li, Y., Deng, X.: A novel quantum-inspired immune clonal algorithm with the evolutionary game approach. Progr. Nat. Sci. 19(10), 1341–1347 (2009)

    Article  MathSciNet  Google Scholar 

  • Xiao, W.X., Zang, X., Yan, X.P.: QGA based bandwidth adaptation scheme for wireless/mobile networks. In: Proc. ITST, pp. 1323–1326 (2006)

  • Xing, H., Ji, Y., Bai, L., Liu, X., Qu, Z., Wang, X.: An adaptive-evolution-based quantum-inspired evolutionary algorithm for QOS multicasting in IP/DWDM networks. Comput. Commun. 32(6), 1086–1094 (2009a)

    Article  Google Scholar 

  • Xing, H., Liu, X., Jin, X., Bai, L., Ji, Y.: A multi-granularity evolution based quantum genetic algorithm for QOS multicast routing problem in WDM networks. Comput. Commun. 32(2), 386–393 (2009b)

    Article  Google Scholar 

  • Xu, J.J., Chen, H.J., Cheng, Y.H., Luo, R.: Blind signal separation based on quantum genetic algorithm. J. Commun. Comput. 2(9), 62–66 (2005)

    Google Scholar 

  • Yang, J.A., Li, Z.Q., Zhuang, Z.Q.: Multi-universe parallel quantum genetic algorithm and its application to blind source separation. In: Proc. ICNNS, pp. 393–398 (2003a)

  • Yang, J.A., Peng, H., Zhuang, Z.Q.: Research of nonlinear blind source separation algorithm based on quantum evolutionary neural network. In: Proc. ICMLC, pp. 835–840 (2003b)

  • Yang, J.A., Zhao, B., Ye, Z.F.: Research of blind deconvolution algorithm based on high-order statistics and quantum inspired GA. In: Lecture Notes in Computer Science, vol. 3611, pp. 461–467 (2005)

  • Yang, Q., Ding, S.C.: Methodology and case study of hybrid quantum-inspired evolutionary algorithm for numerical optimization. In: Proc. ICNC, pp. 634–638 (2007)

  • Yang, S.Y., Jiao, L.C.: The quantum evolutionary programming. In: Proc. ICCIMA, pp. 362–367 (2003)

  • Yang, S.Y., Wang, M., Jiao, L.C.: A genetic algorithm based on quantum chromosome. In: Proc. ICSP, pp. 1622–1625 (2004a)

  • Yang, S.Y., Wang, M., Jiao, L.C.: A novel quantum evolutionary algorithm and its application. In: Proc CEC, pp. 820–826 (2004b)

  • Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)

    Article  Google Scholar 

  • You, X., Liu, Y., Shuai, D.: On parallel immune quantum evolutionary algorithm based on learning mechanism and its convergence. In: Lecture Notes in Computer Science, vol. 4221, pp. 903–912 (2006a)

  • You, X., Shuai, D., Liu, S.: Research and implementation of quantum evolution algorithm based on immune theory. In: Proc. WCICA, pp. 3410–3414 (2006b)

  • You, X., Sheng, L., Dianxun, S.: Studying the performance of quantum evolutionary algorithm based on immune theory. In: Lecture Notes in Computer Science, vol. 4490, pp. 1068–1075 (2007)

  • You, X.M., Liu, S., Shuai, D.X.: On improved parallel immune quantum evolutionary algorithm based on learning mechanism. In: Proc. ISDA, pp. 908–913 (2006c)

  • Yu, Y., Tian, Y.F., Yin, Z.F.: Hybrid quantum evolutionary algorithms based on particle swarm theory. In: Proc. IEA, pp. 309–315 (2006)

  • Zhang, G., Rong, H.: Parameter setting of quantum-inspired genetic algorithm based on real observation. In: Lecture Notes in Artificial Intelligence, vol. 4481, pp. 492–499 (2007a)

  • Zhang, G.X., Rong, H.N.: Improved quantum-inspired genetic algorithm based time-frequency analysis of radar emitter signals. In: Lecture Notes in Artificial Intelligence, vol. 4481, pp. 484–491 (2006)

  • Zhang, G.X., Rong, H.N.: Quantum-inspired genetic algorithm based time-frequency atom decomposition. In: Lecture Notes in Computer Science, vol. 4490, pp. 243–250 (2007b)

  • Zhang, G.X., Rong, H.N.: Real-observation quantum-inspired evolutionary algorithm for a class of numerical optimization problems. In: Lecture Notes in Computer Science, vol. 4490, pp. 989–996 (2007c)

  • Zhang, G.X., Jin, W.D., Hu, L.H.: A novel parallel quantum genetic algorithm. In: Proc. PDCAT, pp. 693–697 (2003a)

  • Zhang, G.X., Jin, W.D., Hu, L.Z.: Quantum evolutionary algorithm for multi-objective optimization problems. In: Proc. ISIC, pp. 703–708 (2003b)

  • Zhang, G.X., Jin, W.D., Li, N.: An improved quantum genetic algorithm and its application. In: Lecture Notes in Artificial Intelligence, vol. 2639, pp. 449–452 (2003c)

  • Zhang, G.X., Hu, L.Z., Jin, W.D.: Quantum computing based machine learning method and its application in radar emitter signal recognition. In: Lecture Notes in Artificial Intelligence, vol. 3131, pp. 92–103 (2004a)

  • Zhang, G.X., Hu, L.Z., Jin, W.D.: Resemblance coefficient and a quantum genetic algorithm for feature selection. In: Lecture Notes in Artificial Intelligence, vol. 3245, pp. 155–168 (2004b)

  • Zhang, G.X., Li, N., Jin, W.D., Hu, L.Z.: Novel quantum genetic algorithm and its applications. Front. Electr. Electron. Eng. China 1(1), 31–36 (2006)

    Article  Google Scholar 

  • Zhang, G.X., Gheorghe, M., Wu, C.Z.: A quantum-inspired evolutionary algorithm based on p systems for knapsack problem. Fund. Inf. 87(1), 93–116 (2008)

    MathSciNet  MATH  Google Scholar 

  • Zhang, J.S., Xu, Z.B., Liang, Y.: The whole annealing genetic algorithms and their sufficient and necessary conditions of convergence. Sci. China Ser. E, Technol. Sci. 27(2), 154–164 (1997)

    Google Scholar 

  • Zhang, R., Gao, H.: Improved quantum evolutionary algorithm for combinatorial optimization problem. In: Proc. ICMLC, pp. 3501–3505 (2007a)

  • Zhang, R., Gao, H.: Real-coded quantum evolutionary algorithm for complex functions with high-dimension. In: Proc. ICMA, pp. 2974–2979 (2007b)

  • Zhao, S., Xu, G., Tao, T., Liang, L.: Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks. Comput. Math. Appl. 57(11–12), 2009–2015 (2009)

    Article  Google Scholar 

  • Zhao, Z., Peng, X., Peng, Y., Yu, E.: An effective constraint handling method in quantum-inspired evolutionary algorithm for knapsack problems. WSEAS Trans. Comput. 5(6), 1194–1199 (2006)

    Google Scholar 

  • Zhou, S., Sun, Z.: A new approach belonging to EDAS: Quantum-inspired genetic algorithm with only one chromosome. In: Lecture Notes in Computer Science, vol. 3612, pp. 141–150 (2005)

  • Zhou, W., Zhou, C., Huang, Y., Wang, Y.: Analysis of gene expression data: Application of quantum-inspired evolutionary algorithm to minimum sum-of-squares clustering. In: Lecture Notes in Artificial Intelligence, vol. 3642, pp. 383–391 (2005)

  • Zhou, W.G., Zhou, C.G., Huang, Y.X., Wang, Y.: Analysis of gene expression data: application of quantum-inspired evolutionary algorithm to minimum sum-of-squares clustering. In: Proc. FSLCT, SPIE, vol. 6105, pp. 383–391 (2006a)

  • Zhou, W.G., Zhou, C.G., Liu, G.X., Lv, H.Y., Liang, Y.C.: An improved quantum-inspired evolutionary algorithm for clustering gene expression data. Comput. Methods, pp. 1351–1356 (2006b)

  • Zitzler, E., Laumanns, M.: Problems and test data for multi-objective optimizers. http://www.tik.ee.ethz.ch/zitzler/testdata.html (1999)

  • Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the performance of the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Communication Networks lab (TIK), Swiss Federal Institute of Technology (ETH) (2001)

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  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, P.R. China

    Gexiang Zhang

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This work is partially supported by the National Natural Science Foundation of China (60702026), the Scientific and Technological Funds for Young Scientists of Sichuan (09ZQ-026-040) and the Open Research Fund of Key Laboratory of Signal and Information Processing, Xihua University (SZJJ2009-003).

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Zhang, G. Quantum-inspired evolutionary algorithms: a survey and empirical study. J Heuristics 17, 303–351 (2011). https://doi.org/10.1007/s10732-010-9136-0

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