{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T17:30:57Z","timestamp":1725557457585},"publisher-location":"Berlin, Heidelberg","reference-count":16,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642138027"},{"type":"electronic","value":"9783642138034"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010]]},"DOI":"10.1007\/978-3-642-13803-4_18","type":"book-chapter","created":{"date-parts":[[2010,6,11]],"date-time":"2010-06-11T11:11:36Z","timestamp":1276254696000},"page":"143-150","source":"Crossref","is-referenced-by-count":4,"title":["Scalability of a Methodology for Generating Technical Trading Rules with GAPs Based on Risk-Return Adjustment and Incremental Training"],"prefix":"10.1007","author":[{"given":"E. A.","family":"de la Cal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E. M.","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Quiroga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. R.","family":"Villar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Sedano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. Journal of Financial Economics\u00a0(51), 245\u2013271 (1999)","DOI":"10.1016\/S0304-405X(98)00052-X"},{"key":"18_CR2","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1016\/S0305-0548(03)00063-7","volume":"31","author":"J.Y. Potvin","year":"2004","unstructured":"Potvin, J.Y., Soriano, P., Vall\u00e9e, M.: Generating trading rules on the stock markets with genetic programming. Computers and Operations Research\u00a0(31), 1033\u20131047 (2004)","journal-title":"Computers and Operations Research"},{"issue":"16-18","key":"18_CR3","doi-asserted-by":"publisher","first-page":"3517","DOI":"10.1016\/j.neucom.2008.11.030","volume":"72","author":"T. Chavarnakul","year":"2009","unstructured":"Chavarnakul, T., Enke, D.: A hybrid stock trading system for intelligent technical analysis-based equivolume charting. Neurocomputing\u00a072(16-18), 3517\u20133528 (2009); Financial Engineering; Computational and Ambient Intelligence (IWANN 2007)","journal-title":"Neurocomputing"},{"key":"18_CR4","unstructured":"Neely, C.J.: Risk-adjusted, ex ante, optimal, technical trading rules in equity markets. Technical report, Federal Reserve Bank of St. Louis (2001)"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"O\u2019Neill, M., Brabazon, A., Ryan, C.: Forecasting market indices using evolutionary automatic programming. a case study. In: Genetic Algorithms and Genetic Programming in Computational Finance. University of Limerick, University College Dublin, Ireland (2002)","DOI":"10.1007\/978-1-4615-0835-9_8"},{"key":"18_CR6","unstructured":"Fernandez, M.E., de la Cal, E.A., Quiroga, R.: Improving return using risk-return adjustment and incremental training in technical trading rules with gaps. Applied Intelligence, 1\u201314 (2009)"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Howard, L., D\u2019Angelo, D.: The ga-p: a genetic algorithm and genetic programming hybrid. IEEE Expert, 11\u201315 (1995)","DOI":"10.1109\/64.393137"},{"key":"18_CR8","volume-title":"Genetic Programming: On the programming of computers by means of Natural Selection and Genetic","author":"J. Koza","year":"1992","unstructured":"Koza, J.: Genetic Programming: On the programming of computers by means of Natural Selection and Genetic. MIT Press, Cambridge (1992)"},{"key":"18_CR9","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-662-02830-8","volume-title":"Genetic Algorithms + Data Structures = Evolution Programs","author":"Z. Michalewicz","year":"1992","unstructured":"Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)"},{"key":"18_CR10","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1016\/j.ejor.2005.02.015","volume":"175","author":"P. Xufre Casqueiro","year":"2006","unstructured":"Xufre Casqueiro, P., Rodrigues, A.: Neuro-dynamic trading methods. European Journal of Operational Research\u00a0(175), 1400\u20131412 (2006)","journal-title":"European Journal of Operational Research"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Sharpe, W.F.: Mutual fund performance. Journal of Business. Supplement on Security Prices (39), 119\u201338 (1966)","DOI":"10.1086\/294846"},{"key":"18_CR12","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-5184-0","volume-title":"Evolutionary Algorithms for solving Multi-objective Problems","author":"C. Coello-Coello","year":"2002","unstructured":"Coello-Coello, C., Veldhuizen, V., Lamont, G.B.: Evolutionary Algorithms for solving Multi-objective Problems. Kluwer, Dordrecht (2002)"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Elaoud, S., Loukil, T., Teghem, J.: The pareto fitness genetic algorithm: Test function study. European Journal of Operational Research\u00a0(177), 1703\u20131719 (2007)","DOI":"10.1016\/j.ejor.2005.10.018"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Alcal\u00e1-Fdez, J., S\u00e1nchez, L., Garc\u00eda, S., del Jesus, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fern\u00e1ndez, J., Herrera, F.: Keel: A software tool to assess evolutionary algorithms to data mining problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications (2008) (Online)","DOI":"10.1007\/s00500-008-0323-y"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Ng, H.S., Lam, K.P., Lam, S.S.: Incremental genetic fuzzy expert trading system for derivatives market timing. In: IEEE International Conference on Computational Intelligence for Financial Engineering, Hong-Kong, pp. 421\u2013428 (2003)","DOI":"10.1109\/CIFER.2003.1196291"},{"key":"18_CR16","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1002\/(SICI)1098-111X(199911)14:11<1123::AID-INT4>3.0.CO;2-6","volume":"14","author":"O. Cord\u00f3n","year":"1998","unstructured":"Cord\u00f3n, O., Jesus, M.J.D., Herrera, F., Lozano, M.: Mogul: A methodology to obtain genetic fuzzy rule-based systems under the iterative rule learning approach. International Journal of Intelligent Systems\u00a014, 1123\u20131153 (1998)","journal-title":"International Journal of Intelligent Systems"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligence Systems"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-13803-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T15:07:32Z","timestamp":1685632052000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-13803-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010]]},"ISBN":["9783642138027","9783642138034"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-13803-4_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2010]]}}}