| Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis F Herrera, M Lozano, JL Verdegay Artificial intelligence review 12, 265-319, 1998 | 1827 | 1998 |
| A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization S García, D Molina, M Lozano, F Herrera Journal of Heuristics 15, 617-644, 2009 | 1780 | 2009 |
| Tuning fuzzy logic controllers by genetic algorithms F Herrera, M Lozano, JL Verdegay International Journal of Approximate Reasoning 12 (3-4), 299-315, 1995 | 595 | 1995 |
| A taxonomy for the crossover operator for real‐coded genetic algorithms: An experimental study F Herrera, M Lozano, AM Sánchez International Journal of Intelligent Systems 18 (3), 309-338, 2003 | 519 | 2003 |
| Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study JR Cano, F Herrera, M Lozano IEEE transactions on evolutionary computation 7 (6), 561-575, 2003 | 458 | 2003 |
| Real-coded memetic algorithms with crossover hill-climbing M Lozano, F Herrera, N Krasnogor, D Molina Evolutionary computation 12 (3), 273-302, 2004 | 438 | 2004 |
| Gradual distributed real-coded genetic algorithms F Herrera, M Lozano IEEE transactions on evolutionary computation 4 (1), 43-63, 2000 | 406 | 2000 |
| A learning process for fuzzy control rules using genetic algorithms F Herrera, M Lozano, JL Verdegay Fuzzy sets and systems 100 (1-3), 143-158, 1998 | 324 | 1998 |
| Global and local real-coded genetic algorithms based on parent-centric crossover operators C García-Martínez, M Lozano, F Herrera, D Molina, AM Sánchez European journal of operational research 185 (3), 1088-1113, 2008 | 307 | 2008 |
| Fuzzy connectives based crossover operators to model genetic algorithms population diversity F Herrera, M Lozano, JL Verdegay Fuzzy Sets and Systems 92 (1), 21-30, 1997 | 287 | 1997 |
| Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report M Lozano, C García-Martínez Computers & Operations Research 37 (3), 481-497, 2010 | 256 | 2010 |
| Replacement strategies to preserve useful diversity in steady-state genetic algorithms M Lozano, F Herrera, JR Cano Information sciences 178 (23), 4421-4433, 2008 | 242 | 2008 |
| Memetic algorithms for continuous optimisation based on local search chains D Molina, M Lozano, C Garcia-Martinez, F Herrera Evolutionary computation 18 (1), 27-63, 2010 | 211 | 2010 |
| Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions F Herrera, M Lozano Soft computing 7 (8), 545-562, 2003 | 189 | 2003 |
| Hybrid crossover operators for real-coded genetic algorithms: an experimental study F Herrera, M Lozano, AM Sánchez Soft Computing 9, 280-298, 2005 | 186 | 2005 |
| MOGUL: A methodology to obtain genetic fuzzy rule‐based systems under the iterative rule learning approach O Cordón, MJ del Jesus, F Herrera, M Lozano International Journal of Intelligent Systems 14 (11), 1123-1153, 1999 | 180 | 1999 |
| Stratification for scaling up evolutionary prototype selection JR Cano, F Herrera, M Lozano Pattern Recognition Letters 26 (7), 953-963, 2005 | 158 | 2005 |
| Adaptive genetic operators based on coevolution with fuzzy behaviors F Herrera, M Lozano IEEE Transactions on Evolutionary computation 5 (2), 149-165, 2001 | 134 | 2001 |
| Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability JR Cano, F Herrera, M Lozano Data & Knowledge Engineering 60 (1), 90-108, 2007 | 126 | 2007 |
| Continuous scatter search: an analysis of the integration of some combination methods and improvement strategies F Herrera, M Lozano, D Molina European Journal of Operational Research 169 (2), 450-476, 2006 | 122 | 2006 |
