@article {article,
	title = {Evolutionary Stratified Instance Selection applied to Training Set Selection for Extracting High Precise-Interpretable Classification Rules},
	year = {2008},
	month = {01},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel}
}
@article {610,
	title = {Making CN1 -SD Subgroup Discovery Algorithm Scalable to Large Size Data Sets Using Instance Selection},
	journal = {Expert System with Applications},
	volume = {35},
	number = {4},
	year = {2008},
	pages = {1949-1965},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel and Garc{\'\i}a, Salvador}
}
@inbook {Cano2005,
	title = {Instance Selection Using Evolutionary Algorithms: An Experimental Study},
	booktitle = {Advanced Techniques in Knowledge Discovery and Data Mining},
	year = {2005},
	pages = {127{\textendash}152},
	publisher = {Springer London},
	organization = {Springer London},
	address = {London},
	abstract = {In this chapter, we carry out an empirical study of the performance of four representative evolutionary algorithm models considering two instance-selection perspectives, the prototype selection and the training set selection for data reduction in knowledge discovery. This study includes a comparison between these algorithms and other nonevolutionary instance-selection algorithms. The results show that the evolutionary instance-selection algorithms consistently outperform the nonevolutionary ones, offering two main advantages simultaneously, better instance-reduction rates and higher classification accuracy.},
	isbn = {978-1-84628-183-9},
	doi = {10.1007/1-84628-183-0_5},
	url = {https://doi.org/10.1007/1-84628-183-0_5},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel},
	editor = {Pal, Nikhil R. and Jain, Lakhmi}
}
@inbook {inbook,
	title = {Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms},
	year = {2005},
	month = {01},
	pages = {85-96},
	doi = {10.1007/3-540-32400-3_7},
	author = {Lozano, Manuel and F. Herrera and J. R. Cano}
}
@inbook {Cano2005,
	title = {Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining},
	booktitle = {Evolutionary Computation in Data Mining},
	year = {2005},
	pages = {21{\textendash}39},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. As instance selection can be viewed as a search problem, it could be solved using evolutionary algorithms.},
	isbn = {978-3-540-32358-7},
	doi = {10.1007/3-540-32358-9_2},
	url = {https://doi.org/10.1007/3-540-32358-9_2},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel},
	editor = {Ghosh, Ashish and Jain, Lakhmi C.}
}
@conference {739,
	title = {Selecci{\'o}n Evolutiva de Instancias en Miner{\'\i}a de Datos},
	booktitle = {Workwhop de Miner{\'\i}a de Datos y Aprendizaje Autom{\'a}tico},
	year = {2002},
	month = {01},
	address = {Santander (Espa{\~n}a)},
	author = {J. R. Cano and F. Herrera and Lozano, Manuel}
}
@conference {inproceedings,
	title = {An evolutionary paradigm for designing fuzzy rule-based systems from examples},
	year = {1997},
	month = {10},
	pages = {139 - 144},
	isbn = {0-85296-693-8},
	doi = {10.1049/cp:19971170},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and Lozano, Manuel}
}
