Publications | Daniel Molina profile

D. Molina, Daniel Molina Cabrera, JAVIER POYATOS AMADOR, ENEKO OSABA ICEDO, JAVIER DEL SER LORENTE, FRANCISCO HERRERA TRIGUERO (2022). NATURE- AND BIO-INSPIRED OPTIMIZATION: THE GOOD, THE BAD, THE UGLY AND THE HOPEFUL. DYNA.

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Sergio Alonso, Rosana Montes, Daniel Molina, Iván Palomares, Eugenio Martínez-Cámara, Manuel Chiachio, Juan Chiachio, Francisco J. Melero, Pablo García-Moral, Bárbara Fernández, Cristina Moral, Rosario Marchena, Javier Pérez de Vargas, Francisco Herrera (2021). Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys. Sustainability.

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A.D. Martinez, J. Del Ser, E. Villar-Rodriguez, E. Osaba, J. Poyatos, S. Tabik, D. Molina, F. Herrera (2021). Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges. Information Fusion.

Oscar Gómez, Oscar Ibáñez, Andrea Valsecchi, Enrique Bermejo, Daniel Molina, Oscar Cordón (2020). Performance analysis of real-coded evolutionary algorithms under a computationally expensive optimization scenario: 3D–2D Comparative Radiography. Applied Soft Computing.

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A. Latorre, D. Molina (2020). On the Role of Execution Order in Hybrid Evolutionary Algorithms. 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings.

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Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion.

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Javier Del Ser, Eneko Osaba, Daniel Molina, Xin-She Yang, Sancho Salcedo-Sanz, David Camacho, Swagatam Das, Ponnuthurai N. Suganthan, Carlos A. Coello Coello, Francisco Herrera (2019). Bio-inspired computation: Where we stand and what′s next. Swarm and Evolutionary Computation.

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D. Molina, A.R. Nesterenko, A. Latorre (2019). Comparing Large-Scale Global Optimization Competition winners in a real-world problem. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings.

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D. Molina, A.R. Nesterenko, A. Latorre (2019). Comparing Large-Scale Global Optimization Competition winners in a real-world problem. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings.

J. Del Ser, E. Osaba, D. Molina, X.-S. Yang, S. Salcedo-Sanz, D. Camacho, S. Das, P.N. Suganthan, C.A. Coello Coello, F. Herrera (2019). Bio-inspired computation: Where we stand and what's next. Swarm and Evolutionary Computation.

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D. Molina, F. Herrera (2019). 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 - Proceedings.

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M. Leon, N. Xiong, D. Molina, F. Herrera (2019). A novel memetic framework for enhancing differential evolution algorithms via combination with alopex local search. International Journal of Computational Intelligence Systems.

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D. Molina, A. Latorre (2018). Toolkit for the Automatic Comparison of Optimizers: Comparing Large-Scale Global Optimizers Made Easy. 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings.

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D. Molina, A. LaTorre, F. Herrera (2018). An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions. Cognitive Computation.

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C. García-Martínez, P.D. Gutiérrez, D. Molina, M. Lozano, F. Herrera (2017). Since CEC 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis’s weakness. Soft Computing.

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D. Molina (2017). Since CEC 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis’s weakness. Soft Computing.

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D. Molina, F. Moreno-García, F. Herrera (2017). Analysis among winners of different IEEE CEC competitions on real-parameters optimization: Is there always improvement?. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings.

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D. Molina, F. Moreno-García, F. Herrera (2017). Analysis among winners of different IEEE CEC competitions on real-parameters optimization: Is there always improvement?. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings.

B. Lacroix, D. Molina, F. Herrera (2016). Region-based memetic algorithm with archive for multimodal optimisation. Information Sciences.

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C. Bergmeir, D. Molina, J.M. Benítez (2016). Memetic algorithms with local search chains in R: The Rmalschains package. Journal of Statistical Software.

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D. Molina (2016). Memetic Algorithms with Local Search Chains in R: The Rmalschains Package. Journal of Statistical Software.

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S. Salcedo-Sanz, C. Camacho-Gomez, D. Molina, F. Herrera (2016). A coral reefs optimization algorithm with substrate layers and local search for large scale global optimization. 2016 IEEE Congress on Evolutionary Computation, CEC 2016.

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T. Liao, D. Molina, T. Stützle (2015). Performance evaluation of automatically tuned continuous optimizers on different benchmark sets. Applied Soft Computing Journal.

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D. Molina, F. Herrera (2015). Iterative hybridization of de with Local Search for the CEC'2015 special session on large scale global optimization. 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings.

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N. Xiong, D. Molina, M.L. Ortiz, F. Herrera (2015). A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends. International Journal of Computational Intelligence Systems.

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M. Lastra, D. Molina, J.M. Benítez (2015). A high performance memetic algorithm for extremely high-dimensional problems. Information Sciences.

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B. Lacroix, D. Molina, F. Herrera (2014). Region based memetic algorithm for real-parameter optimisation. Information Sciences.

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D. Molina, B. Lacroix, F. Herrera (2014). Influence of regions on the memetic algorithm for the CEC'2014 Special Session on Real-Parameter Single Objective Optimisation. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014.

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T. Liao, D. Molina, M.A.M. de Oca, T. Stützle (2014). A Note on Bound Constraints Handling for the IEEE CEC'05 Benchmark Function Suite. Evolutionary Computation.

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D. Molina, A. Puris, R. Bello, F. Herrera (2013). Variable mesh optimization for the 2013 CEC Special Session Niching Methods for Multimodal Optimization. 2013 IEEE Congress on Evolutionary Computation, CEC 2013.

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D. Molina, A. Puris, R. Bello, F. Herrera (2013). Variable mesh optimization for the 2013 CEC Special Session Niching Methods for Multimodal Optimization. 2013 IEEE Congress on Evolutionary Computation, CEC 2013.

B. Lacroix, D. Molina, F. Herrera (2013). Dynamically updated region based memetic algorithm for the 2013 CEC Special Session and Competition on Real Parameter Single Objective Optimization. 2013 IEEE Congress on Evolutionary Computation, CEC 2013.

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A. Puris, R. Bello, D. Molina, F. Herrera (2012). Variable mesh optimization for continuous optimization problems. Soft Computing.

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A. Puris, R. Bello, D. Molina, F. Herrera (2012). Variable mesh optimization for continuous optimization problems. Soft Computing.

C. Bergmeir, I. Triguero, D. Molina, J.L. Aznarte, J.M. Benitez (2012). Time series modeling and forecasting using memetic algorithms for regime-switching models. IEEE Transactions on Neural Networks and Learning Systems.

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B. Lacroix, D. Molina, F. Herrera (2012). Region based memetic algorithm with LS chaining. 2012 IEEE Congress on Evolutionary Computation, CEC 2012.

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A. Puris, R. Bello, D. Molina, F. Herrera (2012). Optimising real parameters using the information of a mesh of solutions: VMO algorithm. 2012 IEEE Congress on Evolutionary Computation, CEC 2012.

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J. Marín, D. Molina, F. Herrera (2012). Modeling dynamics of a real-coded CHC algorithm in terms of dynamical probability distributions. Soft Computing.

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T. Liao, D. Molina, T. Stützle, M.A. Montes De Oca, M. Dorigo (2012). An ACO algorithm benchmarked on the BBOB noiseless function testbed. GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion.

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D. Molina, M. Lozano, A.M. Sánchez, F. Herrera (2011). Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains. Soft Computing.

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D. Molina, M. Lozano, A.M. Sánchez, F. Herrera (2011). Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains. Soft Computing.

M. Lozano, D. Molina, C. García-Martínez (2011). Iterated greedy for the maximum diversity problem. European Journal of Operational Research.

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M. Lozano, D. Molina, F. Herrera (2011). Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Computing.

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J.L. Aznarte, D. Molina, A.M. Sánchez, J.M. Benítez (2011). A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters. Applied Intelligence.

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J. Derrac, S. García, D. Molina, F. Herrera (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation.

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D. Molina, M. Lozano, C. García-Martínez, F. Herrera (2010). Memetic algorithms for continuous optimisation based on local search chains. Evolutionary Computation.

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D. Molina, M. Lozano, C. García-Mart\ńez, F. Herrera (2010). Memetic algorithms for continuous optimisation based on local search chains. Evolutionary Computation.

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D Molina, M Lozano, C Garcia-Martinez, F Herrera (2010). Memetic Algorithms for Continuous Optimisation Based on Local Search Chains. Evolutionary Computation.

D. Molina, M. Lozano, F. Herrera (2009). Study of the influence of the local search method in memetic algorithms for large scale continuous optimization problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

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D. Molina, M. Lozano, F. Herrera (2009). Study of the influence of the local search method in memetic algorithms for large scale continuous optimization problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

D. Molina, M. Lozano, F. Herrer (2009). Memetic algorithm with local search chaining for large scale continuous optimization problems. 2009 IEEE Congress on Evolutionary Computation, CEC 2009.

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D. Molina, M. Lozano, F. Herrera (2009). Memetic algorithm with local search chaining for continuous optimization problems: A scalability test. ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications.

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S. García, D. Molina, M. Lozano, F. Herrera (2009). 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.

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A.M. Sánchez, M. Lozano, C. García-Martínez, D. Molina, F. Herrera (2008). Real-parameter crossover operators with multiple descendents: An experimental study. International Journal of Intelligent Systems.

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A.M. Sánchez, M. Lozano, C. García-Mart\ńez, D. Molina, F. Herrera (2008). Real-parameter crossover operators with multiple descendents: An experimental study. International Journal of Intelligent Systems.

D. Molina, M. Lozano, C. García-Martínez, F. Herrera (2008). Memetic algorithm for intense local search methods using local search chains. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

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C. García-Martínez, M. Lozano, F. Herrera, D. Molina, A.M. Sánchez (2008). Global and local real-coded genetic algorithms based on parent-centric crossover operators. European Journal of Operational Research.

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F. Herrera, M. Lozano, D. Molina (2006). Continuous scatter search: An analysis of the integration of some combination methods and improvement strategies. European Journal of Operational Research.

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C. García-Martínez, M. Lozano, D. Molina (2006). A local genetic algorithm for binary-coded problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).

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D. Molina, F. Herrera, M. Lozano (2005). Adaptive local search parameters for real-coded memetic algorithms. 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings.

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M. Lozano, F. Herrera, N. Krasnogor, D. Molina (2004). Real-coded memetic algorithms with crossover hill-climbing. Evolutionary Computation.

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