Lozano Márquez, M., Herrera Triguero, F., & Cano De Amo, J. R. (2012). Replacement Strategies to Preserve Useful Diversity in Steady-State Genetic Algorithms. Information Sciences.
Manuel Lozano Márquez
First name
Manuel
Last name
Lozano Márquez
2012
2008
Cano De Amo, J. R., Herrera Triguero, F., Lozano Márquez, M., & García López, S. (2008). Making CN2-SD Subgroup Discovery Algorithm scalable to Large Size Data Sets using Instance Selection. Expert Systems with Applications, 35, 1949-1965.
Lozano Márquez, M., Cano De Amo, J. R., & Herrera Triguero, F. (2008). Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Information Sciences, 178, 4421-4433. https://doi.org/10.1016/j.ins.2008.07.031
2007
Cano De Amo, J. R., Herrera Triguero, F., & Lozano Márquez, M. (2007). Evolutionary Stratified Training Set Selection for Extracting Classification Rules with trade off Precision-Interpretability. Data \& Knowledge Engineering, 60, 90-108.
2006
Cano De Amo, J. R., Herrera Triguero, F., & Lozano Márquez, M. (2006). On the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining. Applied Soft Computing, 6, 323-332.
2005
Cano De Amo, J. R., Herrera Triguero, F., & Lozano Márquez, M. (2005). A Study on the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining (F. Hoffmann, M. Köppen, F. Klawonn, & R. Roy, Eds.). Springer-Verlag.
Cano De Amo, J. R., Herrera Triguero, F., & Lozano Márquez, M. (2005). Stratification for Scaling Up Evolutionary Prototype Selection. Pattern Recognition Letters, 26, 953-963.
2004
Cano De Amo, J. R., Herrera Triguero, F., & Lozano Márquez, M. (2004). Selección Evolutiva Estratificada de Conjuntos de Entrenamiento para la Obtención de Bases de Reglas con un Alto Equilibrio entre Precisión e Interpretabilidad (R. Giráldez, J. C. Riquelme, & J. S. Aguilar, Eds.).
2003
Lozano Márquez, M., Herrera Triguero, F., & Cano De Amo, J. R. (2003). Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms. Presentado en. (Original work published 2026)
Cano De Amo, J. R., Herrera Triguero, F., & Lozano Márquez, M. (2003). Using Evolutionary Algorithms as Instance Selection for Data Reduction in KDD: an Experimental Study. IEEE Transactions on Evolutionary Computation, 7, 561-575.