Papers published in Journals (D. Molina)

Number of Results: 37

Jump to Year: 2023, 2022, 2021, 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2012, 2011, 2010, 2009, 2008, 2006, 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 (3)

• [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
  
• [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 (2)

• [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
  

2018 (1)

• [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
  

2017 (1)

• [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
  

2016 (2)

• [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
  
• [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
  
  

2015 (3)

• [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
  

2014 (2)

• [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
  

2012 (3)

• [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
  
• [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
  

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 (1)

• [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
  

2009 (1)

• [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
  
  

2008 (2)

• [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
  

2006 (1)

• [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
  

2004 (1)

• [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