{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T20:31:09Z","timestamp":1694637069425},"reference-count":28,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2009,8,21]],"date-time":"2009-08-21T00:00:00Z","timestamp":1250812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,8,21]]},"abstract":"Purpose<\/jats:title>Following earlier claims that quantum\u2010inspired evolutionary algorithm (QIEA) may offer advantages in high\u2010dimensional environments, the purpose of this paper is to test a real\u2010valued QIEA on a series of benchmark functions of varying dimensionality in order to examine its scalability within both static and dynamic environments.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>This paper compares the performance of both the QIEA and the canonical genetic algorithm (GA) on a series of test benchmark functions.<\/jats:p><\/jats:sec>Findings<\/jats:title>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\u2010valued 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><\/jats:sec>Originality\/value<\/jats:title>This paper suggests that QIEA has utility for real\u2010world high\u2010dimensional problems, particularly within dynamic environments, such as that found in real\u2010time financial trading.<\/jats:p><\/jats:sec>","DOI":"10.1108\/17563780910982716","type":"journal-article","created":{"date-parts":[[2009,10,5]],"date-time":"2009-10-05T14:45:58Z","timestamp":1254753958000},"page":"494-512","source":"Crossref","is-referenced-by-count":0,"title":["A comparative study of the canonical genetic algorithm and a real\u2010valued quantum\u2010inspired evolutionary algorithm"],"prefix":"10.1108","volume":"2","author":[{"given":"Kai","family":"Fan","sequence":"first","affiliation":[]},{"given":"Anthony","family":"Brabazon","sequence":"additional","affiliation":[]},{"given":"Conall","family":"O'Sullivan","sequence":"additional","affiliation":[]},{"given":"Michael","family":"O'Neill","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022021419590505900_b1","unstructured":"Baluja, S. 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(2008a), \u201cBenchmarking the performance of the real\u2010valued quantum\u2010inspired evolutionary algorithm\u201d, Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC), Hong Kong, June 1\u20106, pp. 3092\u20108."},{"key":"key2022021419590505900_b8","doi-asserted-by":"crossref","unstructured":"Fan, K., O'Sullivan, C., Brabazon, A. and O'Neill, M. (2008b), \u201cTesting a quantum\u2010inspired evolutionary algorithm by applying it to non\u2010linear principal component analysis of the implied volatility smile\u201d, Natural Computing in Computational Finance, Springer, Berlin, pp. 89\u2010108.","DOI":"10.1007\/978-3-540-77477-8_6"},{"key":"key2022021419590505900_b10","doi-asserted-by":"crossref","unstructured":"Han, K.\u2010H. and Kim, J.\u2010H. 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