Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual

En algunos estudios se ha encontrado que, muchas veces, los algoritmos gené­ticos con una sola población para la solución de la optimización de portafolio con cardinalidad convergen lentamente y no obtienen los mejores resultados. Una manera de mejorar el desempeño de estos algoritmos ha sido incorp...

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Autores:
Vanegas Gutiérrez, Sergio Iván
Tipo de recurso:
Article of journal
Fecha de publicación:
2024
Institución:
Universidad Externado de Colombia
Repositorio:
Biblioteca Digital Universidad Externado de Colombia
Idioma:
spa
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oai:bdigital.uexternado.edu.co:001/25301
Acceso en línea:
https://bdigital.uexternado.edu.co/handle/001/25301
https://doi.org/10.18601/17941113.n26.05
Palabra clave:
Portfolio optimization;
cardinality;
genetic algorithms;
markowitz portfolio;
evolutionary algorithms;
multiple population
optimización de portafolio;
cardinalidad;
algoritmos genéticos;
portafolio de Markowitz;
algoritmos evolutivos;
población múltiple
Rights
openAccess
License
Sergio Iván Vanegas Gutiérrez - 2024
id uexternad2_03d2872048d835d9dd3baca38c65972b
oai_identifier_str oai:bdigital.uexternado.edu.co:001/25301
network_acronym_str uexternad2
network_name_str Biblioteca Digital Universidad Externado de Colombia
repository_id_str
dc.title.spa.fl_str_mv Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual
dc.title.translated.eng.fl_str_mv Portfolio optimization with cardinality using dual population genetic algorithms
title Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual
spellingShingle Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual
Portfolio optimization;
cardinality;
genetic algorithms;
markowitz portfolio;
evolutionary algorithms;
multiple population
optimización de portafolio;
cardinalidad;
algoritmos genéticos;
portafolio de Markowitz;
algoritmos evolutivos;
población múltiple
title_short Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual
title_full Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual
title_fullStr Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual
title_full_unstemmed Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual
title_sort Optimización de portafolio con cardinalidad usando algoritmos genéticos de población dual
dc.creator.fl_str_mv Vanegas Gutiérrez, Sergio Iván
dc.contributor.author.spa.fl_str_mv Vanegas Gutiérrez, Sergio Iván
dc.subject.eng.fl_str_mv Portfolio optimization;
cardinality;
genetic algorithms;
markowitz portfolio;
evolutionary algorithms;
multiple population
topic Portfolio optimization;
cardinality;
genetic algorithms;
markowitz portfolio;
evolutionary algorithms;
multiple population
optimización de portafolio;
cardinalidad;
algoritmos genéticos;
portafolio de Markowitz;
algoritmos evolutivos;
población múltiple
dc.subject.spa.fl_str_mv optimización de portafolio;
cardinalidad;
algoritmos genéticos;
portafolio de Markowitz;
algoritmos evolutivos;
población múltiple
description En algunos estudios se ha encontrado que, muchas veces, los algoritmos gené­ticos con una sola población para la solución de la optimización de portafolio con cardinalidad convergen lentamente y no obtienen los mejores resultados. Una manera de mejorar el desempeño de estos algoritmos ha sido incorporar una población adicional que actúe como buscador de máximos y mínimos loca­les; de esta manera, se aumenta la probabilidad de encontrar el óptimo global de la solución en un menor tiempo. Este documento busca identificar el rendimiento en muestra y fuera de muestra de un portafolio de activos de renta variable con restricciones de car­dinalidad usando algoritmos genéticos con una sola población y con población doble, estableciendo como universo el índice Dow Jones. Los resultados mues­tran que el desempeño puede verse afectado por los parámetros seleccionados para realizar la optimización, por lo que es importante tener en cuenta el error en la estimación de la media y la varianza del portafolio.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-12-05T12:44:37Z
2025-04-09T17:21:28Z
dc.date.available.none.fl_str_mv 2024-12-05T12:44:37Z
2025-04-09T17:21:28Z
dc.date.issued.none.fl_str_mv 2024-12-05
dc.type.spa.fl_str_mv Artículo de revista
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dc.relation.references.spa.fl_str_mv Amenc, N. y Le sourd, V. (2003). Portfolio Theory and Performance Analysis. John Wiley & Sons Ltd.
Arnone, S., Loraschi, A. y Tettamanzi, A. (1993). A genectic approach to portfolio selection. In Neural Network World (pp. 597-604). https://doi.org/10.1007/978-3- 7091-7535-4_100
Best, M. J. (2017). Quadratic programming with computer programs. In Quadratic Programming with Computer Programs. https://doi.org/10.1201/9781315120881
Chang, T. J., Meade, N., Beasley, J. E. y Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers and Operations Research, 27(13), 1271-1302. https://doi.org/10.1016/S0305-0548(99)00074-X
Christopherson, J. A., Cariño, D. Runge. y Ferson, W. E. (2009). Portfolio performance measurement and benchmarking. McGraw-Hill.
Coley, D. (1999). An introduction to genetic algorithms for scientists and engineers. World Scientific.
Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4), 311-338. https://doi.org/10.1016/S0045-7825(99)00389-8
DeMiguel, V., Garlappi, L. y Uppal, R. (2008). The Society for Financial Studies Optimal versus Naive Diversification: How Inefficient is the 1 / N Portfolio Strategy? Review of Financial Studies, 22(5), 1915-1953. https://doi.org/10.1093/rfs/hhm075
Fabozzi, F. J., Kolm, P. N., Pachamanova, D. A. y Focardi, S. M. (2007). Robust portfolio optimization. Journal of Portfolio Management, 33(Issue 3). https://doi.org/10.3905/jpm.2007.684751
Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34-105.
Febrianti, W., Sidarto, K. A. y Sumarti, N. (2022). Solving constrained mean-variance portfolio optimization problems using spiral optimization algorithm. International Journal of Financial Studies, 11(1), 1. https://doi.org/10.3390/ijfs11010001
Francis, J. y Kim, D. (2013). Modern portfolio theory foundations, analysis and new developments. In Wiley Finance Series (Vol. 4, Issue 1). John Wiley & Sons Ltd.
FRED Economic Data (2023). Market Yield on U.S. Treasury Securities at 1-Month Constant Maturity, Quoted on an Investment Basis. Federal Reserve Economic Data. https://fred.stlouisfed.org/series/dgs1mo
Gen, M. y Cheng, R. (2000). Genetic algorithms and engineering optimization. In Wiley-interscience. John Wiley & Sons Ltd.
Guijarro, F. (2018). A similarity measure for the cardinality constrained frontier in the mean-variance optimization model. Journal of the Operational Research Society, 69(6), 928-945. https://doi.org/10.1057/s41274-017-0276-6
Guijarro, F. y Tsinaslanidis, P. E. (2021). A surrogate similarity measure for the mean-variance frontier optimisation problem under bound and cardinality constraints. Journal of the Operational Research Society, 72(3), 564-579. https://doi.org/10.1080/01605682.2019.1657367
Haupt, R. L. y Haupt, S. E. (2004). Practical genetic algorithms. In Practical Genetic Algorithms. https://doi.org/10.1002/0471671746
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press.
Jalota, H. y Thakur, M. (2018). Genetic algorithm designed for solving portfolio op-timization problems subjected to cardinality constraint. International Journal of System Assurance Engineering and Management, 9(1), 294-305. https://doi. org/10.1007/s13198-017-0574-z
Jimbo, H. C., Ngongo, I. S., Andjiga, N. G., Suzuki, T. y Onana, C. A. (2017). Portfolio optimization under cardinality constraints: A comparative study. Open Journal of Statistics, 07(04), 731-742. https://doi.org/10.4236/ojs.2017.74051
Kalayci, C. B., Polat, O. y Akbay, M. A. (2020). An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm and Evolutionary Computation, 54, 100662. https://doi.org/10.1016/j.swevo.2020.100662
Kolusheva, D. (2008). Out of Sample Performance of Asset Allocation Strategies. SSRN Electronic Journal.
Konstantinou, C., Tzanetos, A. y Dounias, G. (2022). Cardinality constrained portfolio optimization with a hybrid scheme combining a Genetic Algorithm and Sonar Inspired Optimization. Operational Research, 22(3), 2465-2487. https://doi.org/10.1007/s12351-020-00614-1
Kramer, O. (2017). Studies in Computational Intelligence 679 Genetic Algorithm Essentials. Springer-Berlin.
Leyffer, S. y Lee, J. (2012). Mixed Integer Nonlinear Programming, MINLP. In Springer Reference. https://doi.org/10.1007/springerreference_72472
Ling, S. H. y Leung, F. H. F. (2007). An improved genetic algorithm with average-bound crossover and wavelet mutation operations. Soft Computing, 11(1), 7-31. https://doi.org/10.1007/s00500-006-0049-7
Loraschi, A., Tomassini, M., Tettamanzi, A. y Verda, P. (1995). Distributed genetic algo-rithms with an application to portfolio selection problems. Artificial Neural Nets and Genetic Algorithms (Conference paper), 384-387. https://doi.org/10.1007/978- 3-7091-7535-4_100
Maringer, D. G. (2005). Portfolio management with heuristic optimization. In Springer (Ed.), Portfolio Management with Heuristic Optimization (Vol. 00008). Springer. https://doi.org/10.1007/b136219
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91. https://doi. org/10.2307/2975974
Michalewicz, Z. (1996). Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation, 4(1), 1-32. https://doi.org/10.1162/ evco.1996.4.1.1 Mitchell, M. (1999). An Introduction to Genetic Algorithms. The MIT press.
Moral-Escudero, R., Ruiz-Torrubiano, R. y Suárez, A. (2006). Selection of optimal inves-tment portfolios with cardinality constraints. 2006 IEEE Congress on Evolutionary Computation, CEC 2006, 2382-2388. https://doi.org/10.1109/cec.2006.1688603
Ruiz-Torrubiano, R. y Suárez, A. (2010). Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constraints. IEEE Computational Intelligence Magazine, 5(2), 92-107. https://doi.org/10.1109/mci.2010.936308
Ruiz-Torrubiano, R. y Suárez, A. (2015). A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs. Applied Soft Computing Journal, 36, 125-142. https://doi.org/10.1016/j.asoc.2015.06.053
Sabar, N. R. y Song, A. (2014). Dual population genetic algorithm for the cardinality constrained portfolio selection problem. In G. Dick, W. N. Browne, P. Whigham, M. Zhang, L. T. Bui, H. Ishibuchi, … K. Tang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8886, pp. 703-712). Springer International Publishing. https://doi.org/10.1007/978-3-319-13563-2_59
Sabar, N. R., Turky, A., Leenders, M. y Song, A. (2018). Multi-population genetic al-gorithm for cardinality constrained portfolio selection problems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10860 LNCS, 129-140. https://doi. org/10.1007/978-3-319-93698-7_10
Sadjadi, S. J., Gharakhani, M. y Safari, E. (2012). Robust optimization framework for cardinality constrained portfolio problem. Applied Soft Computing Journal, 12(1), 91-99. https://doi.org/10.1016/j.asoc.2011.09.006
Sinha, P., Chandwani, A. y Sinha, T. (2015). Algorithm of construction of optimum portfolio of stocks using genetic algorithm. International Journal of System Assurance Engineering and Management, 6(4), 447-465. https://doi.org/10.1007/s13198-014-0293-7
Sivanandam, S. N. y Deepa, S. N. (2008a). Genetic Algorithms BT–Introduction to Genetic Algorithms (S. N. Sivanandam & S. N. Deepa, Eds, pp. 15-37). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-73190-0_2
Sivanandam, S. N. y Deepa, S. N. (2008b). Introduction to Genetic Algorithms. Springer. https://doi.org/10.1007/978-3-540-73190-0
Soleimani, H., Golmakani, H. R. y Salimi, M. H. (2009). Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. Expert Systems with Applications, 36(3 part 1), 5058-5063. https://doi.org/10.1016/j.eswa.2008.06.007
Woodside-Oriakhi, M., Lucas, C. y Beasley, J. E. (2011). Heuristic algorithms for the cardinality constrained efficient frontier. European Journal of Operational Research, 213(3), 538-550. https://doi.org/10.1016/j.ejor.2011.03.030
Yaman, I. y Erbay Dalkılıç, T. (2021). A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm. Expert Systems with Applications, 169(December 2020), 114517. https://doi.org/10.1016/j.eswa.2020.114517
dc.rights.spa.fl_str_mv Sergio Iván Vanegas Gutiérrez - 2024
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spelling Vanegas Gutiérrez, Sergio Iván2024-12-05T12:44:37Z2025-04-09T17:21:28Z2024-12-05T12:44:37Z2025-04-09T17:21:28Z2024-12-05En algunos estudios se ha encontrado que, muchas veces, los algoritmos gené­ticos con una sola población para la solución de la optimización de portafolio con cardinalidad convergen lentamente y no obtienen los mejores resultados. Una manera de mejorar el desempeño de estos algoritmos ha sido incorporar una población adicional que actúe como buscador de máximos y mínimos loca­les; de esta manera, se aumenta la probabilidad de encontrar el óptimo global de la solución en un menor tiempo. Este documento busca identificar el rendimiento en muestra y fuera de muestra de un portafolio de activos de renta variable con restricciones de car­dinalidad usando algoritmos genéticos con una sola población y con población doble, estableciendo como universo el índice Dow Jones. Los resultados mues­tran que el desempeño puede verse afectado por los parámetros seleccionados para realizar la optimización, por lo que es importante tener en cuenta el error en la estimación de la media y la varianza del portafolio.Some studies have found that genetic algorithms using a single population for portfolio optimization with cardinality constraints often converge slowly and do not achieve the best results. One way to improve the performance of these algorithms has been to incorporate an additional population that acts as a seeker of local maxima and minima; this increases the likelihood of finding the global optimum of the solution in a shorter time. This document seeks to identify the in-sample and out-of-sample perfor­mance of an equity portfolio with cardinality constraints using genetic algo­rithms with a single population and with a double population, using the Dow Jones index as the universe. The results show that performance can be affected by the parameters selected for optimization, so it is important to consider the error in estimating the mean and variance of the portfolio.application/pdf10.18601/17941113.n26.052346-21401794-1113https://bdigital.uexternado.edu.co/handle/001/25301https://doi.org/10.18601/17941113.n26.05spaUniversidad Externado de Colombiahttps://revistas.uexternado.edu.co/index.php/odeon/article/download/10071/17166Núm. 26 , Año 2024 : Enero-Junio1262695ODEONAmenc, N. y Le sourd, V. (2003). Portfolio Theory and Performance Analysis. John Wiley & Sons Ltd.Arnone, S., Loraschi, A. y Tettamanzi, A. (1993). A genectic approach to portfolio selection. In Neural Network World (pp. 597-604). https://doi.org/10.1007/978-3- 7091-7535-4_100Best, M. J. (2017). Quadratic programming with computer programs. In Quadratic Programming with Computer Programs. https://doi.org/10.1201/9781315120881Chang, T. J., Meade, N., Beasley, J. E. y Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimisation. Computers and Operations Research, 27(13), 1271-1302. https://doi.org/10.1016/S0305-0548(99)00074-XChristopherson, J. A., Cariño, D. Runge. y Ferson, W. E. (2009). Portfolio performance measurement and benchmarking. McGraw-Hill.Coley, D. (1999). An introduction to genetic algorithms for scientists and engineers. World Scientific.Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4), 311-338. https://doi.org/10.1016/S0045-7825(99)00389-8DeMiguel, V., Garlappi, L. y Uppal, R. (2008). The Society for Financial Studies Optimal versus Naive Diversification: How Inefficient is the 1 / N Portfolio Strategy? Review of Financial Studies, 22(5), 1915-1953. https://doi.org/10.1093/rfs/hhm075Fabozzi, F. J., Kolm, P. N., Pachamanova, D. A. y Focardi, S. M. (2007). Robust portfolio optimization. Journal of Portfolio Management, 33(Issue 3). https://doi.org/10.3905/jpm.2007.684751Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34-105.Febrianti, W., Sidarto, K. A. y Sumarti, N. (2022). Solving constrained mean-variance portfolio optimization problems using spiral optimization algorithm. International Journal of Financial Studies, 11(1), 1. https://doi.org/10.3390/ijfs11010001Francis, J. y Kim, D. (2013). Modern portfolio theory foundations, analysis and new developments. In Wiley Finance Series (Vol. 4, Issue 1). John Wiley & Sons Ltd.FRED Economic Data (2023). Market Yield on U.S. Treasury Securities at 1-Month Constant Maturity, Quoted on an Investment Basis. Federal Reserve Economic Data. https://fred.stlouisfed.org/series/dgs1moGen, M. y Cheng, R. (2000). Genetic algorithms and engineering optimization. In Wiley-interscience. John Wiley & Sons Ltd.Guijarro, F. (2018). A similarity measure for the cardinality constrained frontier in the mean-variance optimization model. Journal of the Operational Research Society, 69(6), 928-945. https://doi.org/10.1057/s41274-017-0276-6Guijarro, F. y Tsinaslanidis, P. E. (2021). A surrogate similarity measure for the mean-variance frontier optimisation problem under bound and cardinality constraints. Journal of the Operational Research Society, 72(3), 564-579. https://doi.org/10.1080/01605682.2019.1657367Haupt, R. L. y Haupt, S. E. (2004). Practical genetic algorithms. In Practical Genetic Algorithms. https://doi.org/10.1002/0471671746Holland, J. H. (1992). Adaptation in Natural and Artificial Systems. An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press.Jalota, H. y Thakur, M. (2018). Genetic algorithm designed for solving portfolio op-timization problems subjected to cardinality constraint. International Journal of System Assurance Engineering and Management, 9(1), 294-305. https://doi. org/10.1007/s13198-017-0574-zJimbo, H. C., Ngongo, I. S., Andjiga, N. G., Suzuki, T. y Onana, C. A. (2017). Portfolio optimization under cardinality constraints: A comparative study. Open Journal of Statistics, 07(04), 731-742. https://doi.org/10.4236/ojs.2017.74051Kalayci, C. B., Polat, O. y Akbay, M. A. (2020). An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization. Swarm and Evolutionary Computation, 54, 100662. https://doi.org/10.1016/j.swevo.2020.100662Kolusheva, D. (2008). Out of Sample Performance of Asset Allocation Strategies. SSRN Electronic Journal.Konstantinou, C., Tzanetos, A. y Dounias, G. (2022). Cardinality constrained portfolio optimization with a hybrid scheme combining a Genetic Algorithm and Sonar Inspired Optimization. Operational Research, 22(3), 2465-2487. https://doi.org/10.1007/s12351-020-00614-1Kramer, O. (2017). Studies in Computational Intelligence 679 Genetic Algorithm Essentials. Springer-Berlin.Leyffer, S. y Lee, J. (2012). Mixed Integer Nonlinear Programming, MINLP. In Springer Reference. https://doi.org/10.1007/springerreference_72472Ling, S. H. y Leung, F. H. F. (2007). An improved genetic algorithm with average-bound crossover and wavelet mutation operations. Soft Computing, 11(1), 7-31. https://doi.org/10.1007/s00500-006-0049-7Loraschi, A., Tomassini, M., Tettamanzi, A. y Verda, P. (1995). Distributed genetic algo-rithms with an application to portfolio selection problems. Artificial Neural Nets and Genetic Algorithms (Conference paper), 384-387. https://doi.org/10.1007/978- 3-7091-7535-4_100Maringer, D. G. (2005). Portfolio management with heuristic optimization. In Springer (Ed.), Portfolio Management with Heuristic Optimization (Vol. 00008). Springer. https://doi.org/10.1007/b136219Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91. https://doi. org/10.2307/2975974Michalewicz, Z. (1996). Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation, 4(1), 1-32. https://doi.org/10.1162/ evco.1996.4.1.1 Mitchell, M. (1999). An Introduction to Genetic Algorithms. The MIT press.Moral-Escudero, R., Ruiz-Torrubiano, R. y Suárez, A. (2006). Selection of optimal inves-tment portfolios with cardinality constraints. 2006 IEEE Congress on Evolutionary Computation, CEC 2006, 2382-2388. https://doi.org/10.1109/cec.2006.1688603Ruiz-Torrubiano, R. y Suárez, A. (2010). Hybrid approaches and dimensionality reduction for portfolio selection with cardinality constraints. IEEE Computational Intelligence Magazine, 5(2), 92-107. https://doi.org/10.1109/mci.2010.936308Ruiz-Torrubiano, R. y Suárez, A. (2015). A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs. Applied Soft Computing Journal, 36, 125-142. https://doi.org/10.1016/j.asoc.2015.06.053Sabar, N. R. y Song, A. (2014). Dual population genetic algorithm for the cardinality constrained portfolio selection problem. In G. Dick, W. N. Browne, P. Whigham, M. Zhang, L. T. Bui, H. Ishibuchi, … K. Tang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8886, pp. 703-712). Springer International Publishing. https://doi.org/10.1007/978-3-319-13563-2_59Sabar, N. R., Turky, A., Leenders, M. y Song, A. (2018). Multi-population genetic al-gorithm for cardinality constrained portfolio selection problems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10860 LNCS, 129-140. https://doi. org/10.1007/978-3-319-93698-7_10Sadjadi, S. J., Gharakhani, M. y Safari, E. (2012). 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Expert Systems with Applications, 169(December 2020), 114517. https://doi.org/10.1016/j.eswa.2020.114517Sergio Iván Vanegas Gutiérrez - 2024info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.http://creativecommons.org/licenses/by-nc-sa/4.0https://revistas.uexternado.edu.co/index.php/odeon/article/view/10071Portfolio optimization;cardinality;genetic algorithms;markowitz portfolio;evolutionary algorithms;multiple populationoptimización de portafolio;cardinalidad;algoritmos genéticos;portafolio de Markowitz;algoritmos evolutivos;población múltipleOptimización de portafolio con cardinalidad usando algoritmos genéticos de población dualPortfolio optimization with cardinality using dual population genetic algorithmsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2559https://bdigital.uexternado.edu.co/bitstreams/734173f4-257f-4e7e-b8d5-40f8c60e1cf2/download5b875b305125a49010714c9e40253b57MD51001/25301oai:bdigital.uexternado.edu.co:001/253012025-04-09 12:21:28.298http://creativecommons.org/licenses/by-nc-sa/4.0Sergio Iván Vanegas Gutiérrez - 2024https://bdigital.uexternado.edu.coUniversidad Externado de Colombiametabiblioteca@metabiblioteca.org

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