{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T19:04:04Z","timestamp":1754161444245,"version":"3.41.2"},"reference-count":28,"publisher":"Emerald","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,8,21]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>Following earlier claims that quantum-inspired evolutionary algorithm (QIEA) may offer advantages in high-dimensional environments, the purpose of this paper is to test a real-valued QIEA on a series of benchmark functions of varying dimensionality in order to examine its scalability within both static and dynamic environments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>This paper compares the performance of both the QIEA and the canonical genetic algorithm (GA) on a series of test benchmark functions.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>The results show that the QIEA obtains highly competitive results when benchmarked against the GA within static environments, while substantially outperforming both binary and real-valued representation of the GA in terms of running time. Within dynamic environments, the QIEA outperforms GA in terms of stability and run time.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>This paper suggests that QIEA has utility for real-world high-dimensional problems, particularly within dynamic environments, such as that found in real-time financial trading.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/17563780910982716","type":"journal-article","created":{"date-parts":[[2009,10,5]],"date-time":"2009-10-05T10:45:58Z","timestamp":1254739558000},"page":"494-512","source":"Crossref","is-referenced-by-count":0,"title":["A comparative study of the canonical genetic algorithm and a real-valued quantum-inspired evolutionary algorithm"],"prefix":"10.1108","volume":"2","author":[{"given":"Kai","family":"Fan","sequence":"first","affiliation":[{"name":"School of Business and Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland"}]},{"given":"Anthony","family":"Brabazon","sequence":"additional","affiliation":[{"name":"UCD Complex Adaptive Systems Laboratory, School of Business and Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland"}]},{"given":"Conall","family":"O'Sullivan","sequence":"additional","affiliation":[{"name":"School of Business and Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland"}]},{"given":"Michael","family":"O'Neill","sequence":"additional","affiliation":[{"name":"UCD Complex Adaptive Systems Laboratory, School of Computer Science and Informatics and Natural Computing Research and Applications Group, University College Dublin, Dublin, Ireland"}]}],"member":"140","reference":[{"unstructured":"Baluja, S.\n           (1994), \u201cPopulation based incremental learning: a method for integrating genetic search based function optimization and competitive learning\u201d, Technical Report, No. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, PA.","key":"2025072819012257100_b1"},{"unstructured":"Brabazon, A.\n           and O'Neill, M. (2006), Biologically Inspired Algorithms for Financial Modelling, Springer, Berlin.","key":"2025072819012257100_b2"},{"doi-asserted-by":"crossref","unstructured":"Brabazon, A.\n           and O'Neill, M. (Eds) (2008), Natural Computing in Computational Finance, Springer, Berlin.","key":"2025072819012257100_b3","DOI":"10.1007\/978-3-540-77477-8"},{"unstructured":"Brabazon, A.\n           and O'Neill, M. (Eds) (2009), Computational Intelligence in Finance, Springer, Berlin.","key":"2025072819012257100_b4"},{"unstructured":"da Cruz, A.\n          , Vellasco, M. and Pacheco, M. (2006), \u201cQuantum-inspired evolutionary algorithm for numerical optimization\u201d, Proceedings of the 2006 IEEE Congress on Evolutionary Computation (CEC), IEEE Press, Vancouver, pp. 9180-7.","key":"2025072819012257100_b5"},{"doi-asserted-by":"crossref","unstructured":"Fan, K.\n          , Brabazon, A., O'Sullivan, C. and O'Neill, M. (2007), \u201cOption pricing model calibration using a real-valued quantum-inspired evolutionary algorithm\u201d, Genetic and Evolutionary Computation Conference (GECCO), ACM Press, London, pp. 1983-90.","key":"2025072819012257100_b6","DOI":"10.1145\/1276958.1277351"},{"unstructured":"Fan, K.\n          , Brabazon, A., O'Sullivan, C. and O'Neill, M. (2008a), \u201cBenchmarking the performance of the real-valued quantum-inspired evolutionary algorithm\u201d, Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC), Hong Kong, June 1-6, pp. 3092-8.","key":"2025072819012257100_b7"},{"doi-asserted-by":"crossref","unstructured":"Fan, K.\n          , O'Sullivan, C., Brabazon, A. and O'Neill, M. (2008b), \u201cTesting a quantum-inspired evolutionary algorithm by applying it to non-linear principal component analysis of the implied volatility smile\u201d, Natural Computing in Computational Finance, Springer, Berlin, pp. 89-108.","key":"2025072819012257100_b8","DOI":"10.1007\/978-3-540-77477-8_6"},{"issue":"6","key":"2025072819012257100_b10","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TEVC.2002.804320","article-title":"Quantum-inspired evolutionary algorithm for a class of combinatorial optimization","volume":"6","author":"Han","year":"2002","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"2025072819012257100_b11","first-page":"156","article-title":"Quantum-inspired evolutionary algorithms with a new termination criterion, h\u2009\u03f5\u2009gate and two-phase scheme","volume":"8","author":"Han","year":"2002","journal-title":"IEEE Transactions on Evolutionary Computation"},{"doi-asserted-by":"crossref","unstructured":"Harik, G.\n          , Lobo, F. and Goldberg, D. (1998), \u201cThe compact genetic algorithm\u201d, Proceedings of the International Conference on Evolutionary Computation (CEC 1998), IEEE Press, Englewood Cliffs, NJ, pp. 523-8.","key":"2025072819012257100_b12","DOI":"10.1109\/ICEC.1998.700083"},{"unstructured":"Jeremy, S.\n          , de Bonet, C.L. and Isbell, P.V. (1997), \u201cMIMIC: finding optima by estimating probability densities\u201d, Advances in Neural Information Processing System, Vol. 9, pp. 424-31.","key":"2025072819012257100_b13"},{"unstructured":"Khan, N.\n          , Goldberg, D. and Pelikan, M. (2002), \u201cMultiobjective bayesian optimization algorithm\u201d, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Morgan Kaufmann, San Mateo, CA, p. 648.","key":"2025072819012257100_b14"},{"unstructured":"Larranaga, P.\n           and Lozano, J. (Eds) (2001), Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, Kluwer Academic Press, Dordrecht.","key":"2025072819012257100_b15"},{"issue":"3","key":"2025072819012257100_b16","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1162\/evco.1997.5.3.303","article-title":"The equation for response to selection and its use for prediction","volume":"5","author":"Muhlenbein","year":"1997","journal-title":"Evolutionary Computation"},{"doi-asserted-by":"crossref","unstructured":"Muhlenbein, H.\n           and Mahnig, T. (1999), \u201cThe factorized distribution algorithm for additively decomposed functions\u201d, Proceedings of the 1999 Congress on Evolutionary Computation (CEC), IEEE Press, Englewood Cliffs, NJ, pp. 752-9.","key":"2025072819012257100_b17","DOI":"10.1109\/CEC.1999.782008"},{"doi-asserted-by":"crossref","unstructured":"Narayanan, A.\n           and Moore, M. (1996), \u201cQuantum-inspired genetic algorithms\u201d, Proceedings of IEEE International Conference on Evolutionary Computation (CEC), IEEE Press, Englewood Cliffs, NJ, May, pp. 61-6.","key":"2025072819012257100_b18","DOI":"10.1109\/ICEC.1996.542334"},{"unstructured":"Pelikan, M.\n           (2005), Hierarchical Bayesian Optimization Algorithm Toward a New Generation of Evolutionary Algorithms, Springer, Berlin.","key":"2025072819012257100_b20"},{"issue":"3","key":"2025072819012257100_b19","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1162\/106365600750078808","article-title":"Linkage problem, distribution estimation and bayesian networks","volume":"8","author":"Pelikan","year":"2000","journal-title":"Evolutionary Computation"},{"unstructured":"Pelikan, M.\n          , Sastry, K. and Cantu-Paz, E. (Eds) (2006), Scalable Optimization via Probabilistic Modeling, From Algorithms to Applications Series, Springer, Berlin.","key":"2025072819012257100_b21"},{"unstructured":"Thierens, D.\n           and Bosman, P. (2001), \u201cMulti-objective mixture-based iterated density estimation evolutionary algorithms\u201d, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Morgan Kaufmann, San Mateo, CA, pp. 663-70.","key":"2025072819012257100_b24"},{"unstructured":"Yang, S.\n           (2005), \u201cMemory-enhanced univariate marginal distribution algorithms for dynamic optimization problems\u201d, Proceedings of Congress on Evolutionary Computation (CEC), IEEE Press, Englewood Cliffs, NJ, pp. 2560-7.","key":"2025072819012257100_b27"},{"unstructured":"Yang, S.\n          , Wang, M. and Jiao, L. (2004a), \u201cA genetic algorithm based on quantum chromosome\u201d, Proceedings of IEEE International Conference on Signal Processing (ICSP), IEEE Press, Englewood Cliffs, NJ, August 31-September 4, pp. 1622-5.","key":"2025072819012257100_b25"},{"unstructured":"Yang, S.\n          , Wang, M. and Jiao, L. (2004b), \u201cA novel quantum evolutionary algorithm and its application\u201d, Proceedings of IEEE Congress on Evolutionary Computation 2004 (CEC), IEEE Press, Englewood Cliffs, NJ, June 19-23, pp. 820-6.","key":"2025072819012257100_b26"},{"unstructured":"Goldberg, D.E.\n           (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.","key":"2025072819012257100_frd1"},{"unstructured":"Pohlheim, H.\n           (2007), \u201cGeatbx: genetic and evolutionary algorithm toolbox for use with Matlab\u201d, available at: www.geatbx.com.","key":"2025072819012257100_frd2"},{"unstructured":"Tang, K.\n          , Yao, X. and Suganthan, P.N. 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