SCI2S Publications (D. Molina)

Number of Results: 78

Jump to Year: 2023, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004

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2023 (2)

• [3014] J. Poyatos, D. Molina, A. D. Martínez, J. Del Ser, F. Herrera. EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks. Neural Networks, 158 (2023) 59-82. doi: 10.1016/j.neunet.2022.10.011
  
• [3056] J. Poyatos, D. Molina, Aitor Martínez-Seras, J. Del Ser, F. Herrera. Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness. Applied Soft Computing 147 (2023) 110757 1-12. doi: 10.1016/j.asoc.2023.110757
  

2022 (2)

• [2957] D. Molina, J. Poyatos, E. Osaba, J. Del Ser, F. Herrera. Nature-And Bio-Inspired Optimization: The Good, The Bad, The Ugly And The Hopeful. Dyna, 97:2 (2022) 114-117. doi: 10.6036/10331
  
• [2958] X. Liu, N. Wang, D. Molina, F. Herrera. A least square support vector machine approach based on bvRNA-GA for modeling photovoltaic systems. Applied Soft Computing, 117 (2022) 108357. doi: 10.1016/j.asoc.2021.108357
  

2021 (5)

• [2895] E. Osaba, E. Villar-Rodriguez, J. Del Ser, A. J. Nebro, D. Molina, A. La Torre, P.N. Suganthan, C.A. Coello, F. Herrera. A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems. Swarm and Evolutionary Computation.64 (2021), 100888. doi: 10.1016/j.swevo.2021.100888
  
• [2853] A. D. Martinez, J. del Ser, E. Villa-Rodriguez, E. Osaka, J. Poyatos, S. Tabik, D. Molina, F. Herrera. Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons,Recommendations and Challenges. Information Fusion 67 (2021) 161-194. doi: 10.1016/j.inffus.2020.10.014
  
• [2899] S. Alonso, R. Montes, D. Molina, I. Palomares, E. Martínez-Cámara, M. Chiachio, J. Chiachio, F.J. Melero, P. García-Moral, B. Fernández, C. Moral, R. Marchena, J. Pérez de Vargas, F. Herrera. Article Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability 2021, 13(11), 6038. doi: 10.3390/su13116038
  
• [2906] I. Palomares, E. Martínez-Cámara, R. Montes, P. García-Moral, M. Chiachio, J. Chiachio, S. Alonso, F.J. Melero, D. Molina, B. Fernández, C. Moral, R. Marchena, J.P. de Vargas, F. Herrera. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Applied Intelligence, (2021) 51, 6497-6527. doi: 10.1007/s10489-021-02264-y
  
• [2920] A. LaTorre, D. Molina, E. Osaba, J. Poyatos, J. Del Ser, F. Herrera. A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm and Evolutionary Computation, 67 (2021) 100973. doi: 10.1016/j.swevo.2021.100973
  

2020 (4)

• [2736] A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-Robles, D. Molina, R. Benjamins, R. Chatila, F. Herrera. Explainable ArtificialIntelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58 (2020) 82-115. doi: 10.1016/j.inffus.2019.12.012
  
• [2847] D. Molina, J. Poyatos, J.D. Del Ser, S. García, A. Hussain, F. Herrera. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognitive Computation 12:5 (2020) 897-939. doi: 10.1007/s12559-020-09730-8
  
• [2848] A. La Torre, D. Molina. On The Role Of Execution Order In Hybrid Evolutionary Algorithms. IEEE World Congress On Computational Intelligence (WCCI) 2020, Glasgow, United Kingdom, 1-8, July 19-24, 2020. doi: 10.1109/cec48606.2020.9185676
  
• [2852] O. Gómez, O. Ibáñez, A. Valsecchi, E. Bermejo, D. Molina, O. Cordón. Performance analysis of real-coded evolutionary algorithms under a computationally expensive optimization scenario: 3D–2D Comparative Radiography. Applied Soft Computing 97:A (2020) 106793. doi: 10.1016/j.asoc.2020.106793
  

2019 (4)

• [2690] J. Del Ser, E. Osaba, D. Molina, Xin-She Yang, S. Salcedo-Sanz, D. Camacho, S. Das, P. N.Suganthan, C. A.Coello, F. Herrera. Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation 48 (2019) 220-250. doi: 10.1016/j.swevo.2019.04.008
  
• [2691] M. Leon, N. Xiong, D. Molina, F. Herrera. A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search. International Journal of Computational Intelligence Systems 12:2 (2019) 795-808. doi: 10.2991/ijcis.d.190711.001
  
• [2696] D. Molina, F. Herrera. Applying Memetic algorithm with Improved L-SHADE and Local Search Pool for the 100-digit challenge on Single Objective Numerical Optimization. 2019 IEEE Congress on Evolutionary Computation, CEC'2019, Wellington (New Zealand), 7-12, June 10-13, 2019. doi: 10.1109/CEC.2019.8789916
  
• [2697] D. Molina, A.R. Nesterenko, A. LaTorre. Comparing Large-Scale Global Optimization Competition winners in a real-world problem. IEEE Congress on Evolutionary Computation, CEC'2019, Wellington (New Zealand), 351-357, June 10-13, 2019. doi: 10.1109/CEC.2019.8789943
  

2018 (6)

• [2463] D. Molina, A. LaTorre, F. Herrera. An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions. Cognitive Computation (2018) 10:4, 517-544. doi: 10.1007/s12559-018-9554-0
  
• [2507] D. Molina, A. LaTorre, F. Herrera. SHADE con una Búsqueda Local Iterativa para Optimización de Alta Dimensionalidad. Actas del XIII Congreso Español en Metaheurísticas y Algoritmos Evolutivos y BioInspirados, Granada, 741-746, Octubre 22-25, 2018.
  
• [2478] D. Molina, A. LaTorre, F. Herrera. SHADE with Iterative Local Search for Large-Scale Global Optimization. 2018 World Congress on Computational Intelligence (WCCI-2018), 2018 IEEE Conference on Evolutionary Computation (IEEE CEC'2018), Rio de Janeiro (Brasil), 1252-1259, July 8-13, 2018.
  
• [2479] D. Molina, A. LaTorre. Toolkit for the Automatic Comparison of Optimizers: comparing large-scale global optimizers made easy. 2018 World Congress on Computational Intelligence (WCCI-2018), 2018 IEEE Conference on Evolutionary Computation (IEEE CEC'2018), Rio de Janeiro (Brasil), 1229-1236, July 8-13, 2018.
  
• [2518] D. Molina, A. LaTorre,. Toolkit for the Automatic Comparison of Optimizers (TACO): Herramienta online avanzada para comparar metaheurísticas. Actas del XIII Congreso Español en Metaheurísticas y Algoritmos Evolutivos y BioInspirados, Granada, 727-732, Octubre 22-25, 2018.
  
• [2519] D. Molina, A. LaTorre. Toolkit for the Automatic Comparison of Optimizers: Comparing Large-Scale Global Optimizers Made Easy. 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, Rio de Janeiro, 8-13 July 2018, Rio de Janeiro, Brasil, 1229-1236, 2018.. doi: 10.1109/CEC.2018.8477755
  

2017 (3)

• [2253] D. Molina, F. Moreno-García, F. Herrera. Analysis among winners of different IEEE CEC competitions on real-parameters optimization: Is there always improvement?. 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia-San Sebastian (Spain), 805-812, Juny 5-8, 2017.. doi: 10.1109/CEC.2017.7969392
  
• [2355] C. García-Martínez, P.D. Gutiérrez, D. Molina, M. Lozano, F. Herrera. Since CEC 2005 competition on realparameter optimisation: a decade of research, progress and comparative analysis's weakness. Soft Computing, 21:19 (2017) 5573-5583. doi: 10.1007/s00500-016-2471-9
  
• [2713] D. Molina, F. Moreno-Garcia, F. Herrera. ¿Existe una mejora continua entre los algoritmos ganadores de las competiciones de optimización real del IEEE CEC?. Actas del 12ª Metaheuristics International Conference, XII Congreso de Metaheurísticas, Algoritmos Evolutivos y Bio-inspirados (MAEB'2017), Barcelona (Spain), 771-780, 4-7 July, 2017.
  

2016 (4)

• [2109] B. Lacroix, D. Molina, F. Herrera. Region-based memetic algorithm with archive for multimodal optimisation. Information Sciences 367-368 (2016) 719-746. doi: 10.1016/j.ins.2016.05.049
  
• [2352] S. Salcedo-Sanz, C. Camacho-Gomez, D. Molina, F. Herrera. A coral reefs optimization algorithm with substrate layers and local search for large scale global optimization. 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, BC, Canada, July 24-29, 3574-3581, 2016.. doi: 10.1109/CEC.2016.7744242
  
• [2353] C. Bergmeir, D. Molina, J.M. Benitez. Memetic algorithms with local search chains in R: The Rmalschains package. Journal of Statistical Software. 75:1 (2016) 1-33.. doi: 10.18637/jss.v075.i04
  R source code of the package, replication code from the manuscript
  
  
• [2367] D. Molina, F. Herrera. Descomposición Jerárquica no homogénea de nichos basados en regiones. Actas de las XVIII Conferencia de la Asociación Española para la Inteligencia Artificial, CAEPIA, XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), Salamanca, 439-448, 13-16 Septiembre,2016.
  

2015 (4)

• [1822] M. Lastra, D. Molina, J.M. Benítez. A high performance memetic algorithm for extremely high-dimensional problems. Information Sciences, 293 (2015) 35-58. doi: 10.1016/j.ins.2014.09.018
  
• [1856] Tianjun Liao, D. Molina, Thomas Stützle. Performance evaluation of automatically tuned continuous optimizers on different benchmark sets. Applied Soft Computing, 27 (2015) 490–503. doi: 10.1016/j.asoc.2014.11.006
  
• [1897] Ning Xiong, D. Molina, Miguel Leon Ortiz, F. Herrera. A Walk into Metaheuristic for Engineering Optimization: Principles, Methods and Recent Trends. International Journal of Computational Intelligence Systems 8:4 (2015) 606-636. doi: 10.1080/18756891.2015.1046324
  
• [2067] D. Molina, F. Herrera. Iterative hybridization of DE with local search for the CEC'2015 special session on large scale global optimization. Evolutionary Computation (CEC), 2015 IEEE Congress on Sendai. 25-28 May 2015 1974-1978. doi: 10.1109/CEC.2015.7257127
  

2014 (3)

• [1706] B. Lacroix, D. Molina, F. Herrera. Region based memetic algorithm for real-parameter optimisation. Information Sciences, 262 (2014) 15-31. doi: 10.1016/j.ins.2013.11.032
  
• [1729] Liao Tianjun, D. Molina, Marco A. Montes de Oca, Thomas Stützle. A Note on Bound Constraints Handling for the IEEE CEC’05 Benchmark Function Suite. Evolutionary Computation 22:2 (2014) 351-359. doi: 10.1162/EVCO_a_00120
  
• [1812] D. Molina, B. Lacroix, F. Herrera. Influence of Regions on the Memetic Algorithm for the Special Session on Real-Parameter Single Objetive Optimisation. In Proceeding on the WCCI 2014 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionaty Computation CEC'2010, Beijing (China), 6-11 July, 2014, pp. 1633-1640..
  

2013 (3)

• [1663] B. Lacroix, D. Molina, F. Herrera. Dynamically updated region based memetic algorithm for the 2013 CEC Special Session and Competition on Real Parameter Single Objective Optimization. Evolutionary Computation (CEC), 2013 IEEE Congress on, Cancun (Mexico), pp 1945-1951. doi: 10.1109/CEC.2013.6557797
  
• [1664] D. Molina, Amilkar Puris, Rafael Bello, F. Herrera. Variable mesh optimization for the 2013 CEC Special Session Niching Methods for Multimodal Optimization. Actas en Evolutionary Computation (CEC), 2013 IEEE Congress on, Cancun (Mexico), pp 87-94. doi: 10.1109/CEC.2013.6557557
  
• [1835] D. Molina, Amilkar Puris, Rafael Bello, F. Herrera. Optimización basada en Malla Variable para funciones multimodales. Actas de la Multiconferencia CAEPIA 2013, IX Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB13), Madrid, 17-20 Septiembre 2013, pp 594-603..
  

2012 (9)

• [1414] J. Marín, D. Molina, F. Herrera. Modeling Dynamics of a Real-coded CHC Algorithm in Terms of Dynamical Probability Distributions. Soft Computing 16:2 (2012) 331-351. doi: 10.1007/s00500-011-0745-9
  
• [1433] Amilkar Puris, Rafael Bello, D. Molina, F. Herrera. Variable mesh optimization for continuous optimization problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications (2012), 16(3), 511-525. doi: 10.1007/s00500-011-0753-9
  
• [1504] J. Derrac, S. García, D. Molina, F. Herrera. Un tutorial sobre el uso de test estadísticos no paramétricos en comparaciones múltiples de metaheurísticas y algoritmos evolutivos. Actas del VIII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB12), Albacete (Spain), pp. 455-462, Febrero 8-10, 2012.
  
• [1509] D. Molina, B. Lacroix, F. Herrera. Algoritmo Memético con regiones basado en encadenamiento de búsquedas locales. Actas del VIII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB12), Albacete (Spain), pp. 205-210, Febrero 8-10, 2012.
  
• [1510] D. Molina, Juan Manuel Dodero. Técnicas de pruebas automáticas para algoritmos evolutivos. Actas del VIII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB12), Albacete (Spain), pp. 479-486, Febrero 8-10, 2012.
  
• [1547] B. Lacroix, D. Molina, F. Herrera. Region Based Memetic Algorithm with LS chaining. Actas en WCCI 2012 IEEE World Congress on Computational Intelligence, IEEE CEC, Brisbane (Australia), pp. 1474-1479, Junio 10-15, 2012..
  
• [1548] A. Puris, R. Bello, D. Molina, F. Herrera. Optimising real parameters using the information of a mesh of solutions: VMO algorithm. Actas del WCCI 2012 IEEE World Congress on Computational Intelligence, IEEE CEC, Brisbane (Australia), pp. 2553-2559, June 10-15, 2012.
  
• [1550] C. Bergmeir, I. Triguero, D. Molina, J.L. Aznarte M., J.M. Benítez. Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-switching Models. IEEE Transactions on Neural Networks and Learning Systems (2012), volume 23, issue 11, pages 1841-1847. doi: 10.1109/TNNLS.2012.2216898
  
• [1808] Tianjun Liao, D. Molina, Thomas Stutzle, Marco A. Montes de Oca, Marco Dorigo. An ACO Algorithm Benchmarked on the BBOB Noiseless Function Testbed. Proceedings of the 14th annual conference companion on Genetic and evolutionary computation (GECCO '12).
  

2011 (5)

• [1323] D. Molina, M. Lozano, A.M. Sánchez, F. Herrera. Memetic Algorithms Based on Local Search Chains for Large Scale Continuous Optimisation Problems: MA-SSW-Chains . Soft Computing, 15 (2011) 2201-2220. doi: 10.1007/s00500-010-0647-2
  
• [1374] J. Derrac, S. García, D. Molina, F. Herrera. A Practical Tutorial on the Use of Nonparametric Statistical Tests as a Methodology for Comparing Evolutionary and Swarm Intelligence Algorithms. Swarm and Evolutionary Computation 1:1 (2011) 3-18. doi: 10.1016/j.swevo.2011.02.002
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [1410] M. Lozano, D. Molina, C. García-Martínez. Iterated greedy for the maximum diversity problem. European Journal of Operational Research, 214, (2011), 31-38. doi: 10.1016/j.ejor.2011.04.018
  
• [1436] J.L. Aznarte M., D. Molina, A.M. Sánchez, J.M. Benítez. A test for the homoscedasticity of the residuals in fuzzy rule-based models. Applied Intelligence, 34:3 (2011), 386--393. doi: 10.1016/j.ejor.2011.04.018
  
• [1821] M. Lozano, D. Molina, F. Herrera. Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Computing 15:11 (2011) 2085-2087. doi: 10.1007/s00500-010-0639-2
  

2010 (3)

• [0849] D. Molina, M. Lozano, C. García-Martínez, F. Herrera. Memetic Algorithms for Continuous Optimization Based on Local Search Chains. Evolutionary Computation, 18:1 (2010) 27-63. doi: 10.1162/evco.2010.18.1.18102
  
• [1290] D. Molina, M. Lozano, F. Herrera. MA-SW-Chains: Memetic Algorithm Based on Local Search Chains for Large Scale Continuous Global Optimization. In Proceeding on the WCCI 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionaty Computation CEC'2010, Barcelona (Spain), 18-23 July, pp 3153-3160, 2010. COMPETITION WINNER.. doi: 10.1109%2FCEC.2010.5586034
  
• [1330] D. Molina, C. García-Martínez, M. Lozano, F. Herrera. web temática sobre algoritmos evolutivos y otras metaheurísticas. Proceeding del VII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados, MAEB'2010, Valencia, Septiembre 2010, pp 311-317, 2010.
  

2009 (7)

• [0834] S. García, D. Molina, M. Lozano, F. Herrera. 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. Journal of Heuristics 15 (2009) 617-644. doi: 10.1007/s10732-008-9080-4
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [0964] D. Molina, M. Lozano, F. Herrera. Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems. Learning and Intelligent OptimizatioN Workshop (LION'3), pages 221-234.
  
• [1058] D. Molina, M. Lozano, F. Herrera. Memetic Algorithm with Local Search Chaining for Large Scale Continuous Optimization. Proceeding IEEE Congress on Evolutionary Computation (2009) 830-837.
  
• [1075] D. Molina, M. Lozano, F. Herrera. A Memetic Algorithm using Local Search Chaining for Black-Box Optimization for Noiseless Functions. Proceeding of Genetic and Evolutionary Computation Conference (GECCO), (2009), 2255-2262.
  
• [1076] D. Molina, M. Lozano, F. Herrera. A Memetic Algorithm using Local Search Chaining for Black-Box Optimization for Noisy Functions. Proceeding of Genetic and Evolutionary Computation Conference (GECCO), (2009), 2359-2366.
  
• [1098] D. Molina, M. Lozano, F. Herrera. Algoritmo Memético Basado en Encadenamiento de Búsquedas Locales para Problemas de Optimización Continua. Proceeding VI Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB'09), (2009), 481-486.
  
• [1222] D. Molina, M. Lozano, F. Herrera. Memetic Algorithm with Local Search Chaining for Continuous Optimization Problems. Proceeding of 9th International Conference on Intelligent System Design and Applications, 2009 (ISDA'2009), 1068-1073.
  

2008 (3)

• [0526] C. García-Martínez, M. Lozano, F. Herrera, D. Molina, A.M. Sánchez. Global and Local Real-Coded Genetic Algorithms Based on Parent-Centric Crossover Operators. European Journal of Operational Research 185 (2008) 1088-1113. doi: 10.1016/j.ejor.2006.06.043
  
• [0669] A.M. Sánchez, M. Lozano, C. García-Martínez, D. Molina, F. Herrera. Real-Parameter Crossover Operators with Multiple Descendents: An Experimental Study. International Journal of Intelligent Systems 23:2 (2008) 246-268. doi: 10.1002/int.20258
  
• [0904] D. Molina, M. Lozano, C. García-Martínez, F. Herrera. Memetic Algorithms for Intense Local Search Methods Using Local Search Chains . Proceedings of the International Workshop on Hybrid Metaheuristics (HM 2008), Málaga (Spain), 8-9 october 2008, LNCS 5296, pp. 58-71. doi: 10.1007/978-3-540-88439-2_5
  

2007 (4)

• [0630] S. García, D. Molina, M. Lozano, F. Herrera. Un estudio experimental sobre el uso de test no paramétricos para analizar el comportamiento de los algoritmos evolutivos en problemas de optimización. In Proceedings Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB07), Tenerife (Spain), 275-285, 14-16 February 2007.
  
• [0718] D. Molina, F. Herrera, M. Lozano. Algoritmo Memético con Intensidad de Busqueda Local Adaptativa. Proceedings of the II Congreso Español de Informática (CEDI 2007). I Jornadas sobre Algoritmos Evolutivos y Metaheurísticas (JAEM'07), Zaragoza (Spain), 195-202, 11-14 September 2007.
  
• [0719] S. García, D. Molina, F. Herrera, M. Lozano. Tests no paramétricos de comparaciones múltiples con algoritmo de control en el análisis de algoritmos evolutivos: Un caso de estudio con los resultados de la sesión especial en optimización continua. Proceedings of the II Congreso Español de Informática (CEDI 2007). I Jornadas sobre Algoritmos Evolutivos y Metaheurísticas (JAEM'07), Zaragoza (Spain), 219-227, 11-14 September 2007.
  
• [0755] D. Molina. Algoritmos Meméticos con aplicación adaptativa de la Búsqueda Local para optimización contínua.. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada. October 2007.
  Advisor: Dr. Manuel Lozano and Dr. Francisco Herrera
  

2006 (2)

• [0342] F. Herrera, M. Lozano, D. Molina. Continuous Scatter Search: An Analysis of the Integration of Some Combination Methods and Improvement Strategies. European Journal of Operational Research 169:2 (2006) 450-476. doi: 10.1016/j.ejor.2004.08.009
  
• [0610] C. García-Martínez, M. Lozano, D. Molina. A Local Genetic Algorithm for Binary-coded Problems. Proceedings of the 9th Internationational Conference on Parallel Problem Solving from Nature. Lecture Notes in Computer Science 4193, Springer-Verlag 2006, Reykjavik (Islandia) 192-201, September 2006.
  

2005 (3)

• [0477] D. Molina, F. Herrera, M. Lozano, C. García-Martínez, A.M. Sánchez. Técnicas de Diversidad para Algoritmos Meméticos. Proceeding del Segundo Congreso Mexicano de Computación Evolutiva "COMCEV 2005", 2005, 29-34.
  
• [0493] D. Molina, F. Herrera, M. Lozano. Adaptive Local Search Parameters for Real-Coded Memetic Algorithms. 2005 IEEE Congress on Evolutionary Computation, 2-5 September 2005, Edinburg (Scotland), 888-895.
  
• [0500] D. Molina, F. Herrera, M. Lozano. Técnicas de Diversidad para Algoritmos Meméticos: un estudio experimental. In Proceedings del MAEB'05 (Cuarto congreso español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados), Granada (Spain), 39-46, 2005.
  

2004 (2)

• [0372] M. Lozano, F. Herrera, N. Krasnogor, D. Molina. Algoritmos Meméticos con Codificación Real con Técnicas de Ascensión de Colinas Basadas en el Cruce. In Proceedings del MAEB'04 (Tercer congreso español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados), Córdoba (Spain), 254-261, 2004.
  
• [0337] M. Lozano, F. Herrera, N. Krasnogor, D. Molina. Real-Coded Memetic Algorithms with Crossover Hill-Climbing. Evolutionary Computation 12:3 (2004) 273-302. doi: 10.1162/1063656041774983