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Daniel Molina
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2020 – today
- 2024
- [j35]Isaac Triguero
, Daniel Molina, Javier Poyatos
General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance. Inf. Fusion 103: 102135 (2024) - [i10]Javier Poyatos, Javier Del Ser, Salvador García, Hisao Ishibuchi, Daniel Molina, Isaac Triguero, Bing Xue, Xin Yao, Francisco Herrera:
Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects. CoRR abs/2407.08745 (2024) - [i9]Eneko Osaba, Esther Villar-Rodriguez, Javier Del Ser, Antonio J. Nebro, Daniel Molina, Antonio LaTorre, Ponnuthurai N. Suganthan, Carlos A. Coello Coello, Francisco Herrera:
A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems. CoRR abs/2410.03205 (2024) - 2023
- [j34]Javier Poyatos
Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness. Appl. Soft Comput. 147: 110757 (2023) - [j33]Javier Poyatos
EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks. Neural Networks 158: 59-82 (2023) - [i8]Javier Poyatos, Daniel Molina, Aitor Martínez-Seras, Javier Del Ser, Francisco Herrera:
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness. CoRR abs/2302.10253 (2023) - [i7]Isaac Triguero
General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Open Challenges and Implications. CoRR abs/2307.14283 (2023) - 2022
- [j32]Xiu Liu, Ning Wang, Daniel Molina
A least square support vector machine approach based on bvRNA-GA for modeling photovoltaic systems. Appl. Soft Comput. 117: 108357 (2022) - [i6]Javier Poyatos, Daniel Molina, Aritz D. Martinez, Javier Del Ser, Francisco Herrera:
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks. CoRR abs/2202.03844 (2022) - 2021
- [j31]Iván Palomares, Eugenio Martínez-Cámara
A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Appl. Intell. 51(9): 6497-6527 (2021) - [j30]Aritz D. Martinez
Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges. Inf. Fusion 67: 161-194 (2021) - [j29]Eneko Osaba
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems. Swarm Evol. Comput. 64: 100888 (2021) - [j28]Antonio LaTorre
A prescription of methodological guidelines for comparing bio-inspired optimization algorithms. Swarm Evol. Comput. 67: 100973 (2021) - [c30]Javier Del Ser, Eneko Osaba, Aritz D. Martinez
More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems. SSCI 2021: 1-7 - 2020
- [j27]Óscar Gómez, Óscar Ibáñez, Andrea Valsecchi, Enrique Bermejo Nievas
Performance analysis of real-coded evolutionary algorithms under a computationally expensive optimization scenario: 3D-2D Comparative Radiography. Appl. Soft Comput. 97(Part): 106793 (2020) - [j26]Daniel Molina
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cogn. Comput. 12(5): 897-939 (2020) - [j25]Alejandro Barredo Arrieta, Natalia Díaz Rodríguez
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58: 82-115 (2020) - [c29]Antonio LaTorre
On The Role Of Execution Order In Hybrid Evolutionary Algorithms. CEC 2020: 1-8 - [c28]Cosimo Stallo, Alessandro Neri
Geo-Distributed Simulation and Verification Infrastructure for safe train Galileo-based positioning. ENC 2020: 1-10 - [c27]Daniel Molina, Kislay Kumar, Siddharth Srivastava:
Learn and Link: Learning Critical Regions for Efficient Planning. ICRA 2020: 10605-10611 - [i5]Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, Francisco Herrera:
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations. CoRR abs/2002.08136 (2020) - [i4]Antonio LaTorre, Daniel Molina, Eneko Osaba, Javier Del Ser, Francisco Herrera:
Fairness in Bio-inspired Optimization Research: A Prescription of Methodological Guidelines for Comparing Meta-heuristics. CoRR abs/2004.09969 (2020) - [i3]Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez, Eneko Osaba, Javier Poyatos, Siham Tabik, Daniel Molina, Francisco Herrera:
Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges. CoRR abs/2008.03620 (2020)
2010 – 2019
- 2019
- [j24]Miguel León Ortiz, Ning Xiong, Daniel Molina
A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination With Alopex Local Search. Int. J. Comput. Intell. Syst. 12(2): 795-808 (2019) - [j23]Javier Del Ser
Bio-inspired computation: Where we stand and what's next. Swarm Evol. Comput. 48: 220-250 (2019) - [c26]Daniel Molina
Applying Memetic algorithm with Improved L-SHADE and Local Search Pool for the 100-digit challenge on Single Objective Numerical Optimization. CEC 2019: 7-13 - [c25]Daniel Molina
Comparing Large-Scale Global Optimization Competition winners in a real-world problem. CEC 2019: 359-365 - [i2]Daniel Molina, Kislay Kumar, Siddharth Srivastava:
Identifying Critical Regions for Motion Planning using Auto-Generated Saliency Labels with Convolutional Neural Networks. CoRR abs/1903.03258 (2019) - [i1]Alejandro Barredo Arrieta, Natalia Díaz Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. CoRR abs/1910.10045 (2019) - 2018
- [j22]Daniel Molina
An Insight into Bio-inspired and Evolutionary Algorithms for Global Optimization: Review, Analysis, and Lessons Learnt over a Decade of Competitions. Cogn. Comput. 10(4): 517-544 (2018) - [c24]Daniel Molina
Toolkit for the Automatic Comparison of Optimizers: Comparing Large-Scale Global Optimizers Made Easy. CEC 2018: 1-8 - [c23]Daniel Molina
SHADE with Iterative Local Search for Large-Scale Global Optimization. CEC 2018: 1-8 - 2017
- [j21]Carlos García-Martínez
Since CEC 2005 competition on real-parameter optimisation: a decade of research, progress and comparative analysis's weakness. Soft Comput. 21(19): 5573-5583 (2017) - [c22]Daniel Molina
Analysis among winners of different IEEE CEC competitions on real-parameters optimization: Is there always improvement? CEC 2017: 805-812 - 2016
- [j20]Benjamin Lacroix, Daniel Molina
Region-based memetic algorithm with archive for multimodal optimisation. Inf. Sci. 367-368: 719-746 (2016) - [c21]Sancho Salcedo-Sanz, Carlos Camacho-Gómez, Daniel Molina
A coral reefs optimization algorithm with substrate layers and local search for large scale global optimization. CEC 2016: 3574-3581 - 2015
- [j19]Tianjun Liao, Daniel Molina
Performance evaluation of automatically tuned continuous optimizers on different benchmark sets. Appl. Soft Comput. 27: 490-503 (2015) - [j18]Ning Xiong, Daniel Molina
A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends. Int. J. Comput. Intell. Syst. 8(4): 606-636 (2015) - [j17]Miguel Lastra
A high performance memetic algorithm for extremely high-dimensional problems. Inf. Sci. 293: 35-58 (2015) - [c20]Daniel Molina
Iterative hybridization of DE with Local Search for the CEC'2015 special session on large scale global optimization. CEC 2015: 1974-1978 - 2014
- [j16]Tianjun Liao, Daniel Molina
A Note on Bound Constraints Handling for the IEEE CEC'05 Benchmark Function Suite. Evol. Comput. 22(2): 351-359 (2014) - [j15]Benjamin Lacroix, Daniel Molina
Region based memetic algorithm for real-parameter optimisation. Inf. Sci. 262: 15-31 (2014) - [c19]Daniel Molina
Influence of regions on the memetic algorithm for the CEC'2014 Special Session on Real-Parameter Single Objective Optimisation. IEEE Congress on Evolutionary Computation 2014: 1633-1640 - 2013
- [c18]Daniel Molina
Variable mesh optimization for the 2013 CEC Special Session Niching Methods for Multimodal Optimization. IEEE Congress on Evolutionary Computation 2013: 87-94 - [c17]Benjamin Lacroix, Daniel Molina
Dynamically updated region based memetic algorithm for the 2013 CEC Special Session and Competition on Real Parameter Single Objective Optimization. IEEE Congress on Evolutionary Computation 2013: 1945-1951 - 2012
- [j14]Jesús Marín, Daniel Molina
Modeling dynamics of a real-coded CHC algorithm in terms of dynamical probability distributions. Soft Comput. 16(2): 331-351 (2012) - [j13]Amilkar Puris
Variable mesh optimization for continuous optimization problems. Soft Comput. 16(3): 511-525 (2012) - [j12]Christoph Bergmeir
Time Series Modeling and Forecasting Using Memetic Algorithms for Regime-Switching Models. IEEE Trans. Neural Networks Learn. Syst. 23(11): 1841-1847 (2012) - [c16]Benjamin Lacroix, Daniel Molina
Region based memetic algorithm with LS chaining. IEEE Congress on Evolutionary Computation 2012: 1-6 - [c15]Amilkar Puris
Optimising real parameters using the information of a mesh of solutions: VMO algorithm. IEEE Congress on Evolutionary Computation 2012: 1-7 - [c14]Tianjun Liao, Daniel Molina
An ACO algorithm benchmarked on the BBOB noiseless function testbed. GECCO (Companion) 2012: 159-166 - [c13]Jaan Praks
Boreal forest tree height estimation from interferometric TanDEM-X images. IGARSS 2012: 1262-1265 - [c12]Jaime Tamarit, Daniel Molina, Jose Bueno:
Validation of reference laboratories to put in service Railway projects (tracks and trains). Intelligent Vehicles Symposium 2012: 833-836 - 2011
- [j11]José Luis Aznarte
A test for the homoscedasticity of the residuals in fuzzy rule-based forecasters. Appl. Intell. 34(3): 386-393 (2011) - [j10]Manuel Lozano
Iterated greedy for the maximum diversity problem. Eur. J. Oper. Res. 214(1): 31-38 (2011) - [j9]Manuel Lozano
Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Comput. 15(11): 2085-2087 (2011) - [j8]Daniel Molina
Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains. Soft Comput. 15(11): 2201-2220 (2011) - [j7]Joaquín Derrac, Salvador García
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1): 3-18 (2011) - 2010
- [j6]Daniel Molina
Memetic Algorithms for Continuous Optimisation Based on Local Search Chains. Evol. Comput. 18(1): 27-63 (2010) - [c11]Daniel Molina
MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization. IEEE Congress on Evolutionary Computation 2010: 1-8
2000 – 2009
- 2009
- [j5]Salvador García
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. J. Heuristics 15(6): 617-644 (2009) - [c10]Daniel Molina
Memetic algorithm with Local search chaining for large scale continuous optimization problems. IEEE Congress on Evolutionary Computation 2009: 830-837 - [c9]Daniel Molina, Manuel Lozano, Francisco Herrera:
A memetic algorithm using local search chaining forblack-box optimization benchmarking 2009 for noise free functions. GECCO (Companion) 2009: 2255-2262 - [c8]Daniel Molina, Manuel Lozano, Francisco Herrera:
A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functions. GECCO (Companion) 2009: 2359-2366 - [c7]Daniel Molina
Memetic Algorithm with Local Search Chaining for Continuous Optimization Problems: A Scalability Test. ISDA 2009: 1068-1073 - [c6]Daniel Molina
Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems. LION 2009: 221-234 - [c5]Carlos Angel Iglesias
VMAP: A Dublin Core Application Profile for Musical Resources. MTSR 2009: 1-12 - 2008
- [j4]Carlos García-Martínez
Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur. J. Oper. Res. 185(3): 1088-1113 (2008) - [j3]Ana M. Sánchez
Real-parameter crossover operators with multiple descendents: An experimental study. Int. J. Intell. Syst. 23(2): 246-268 (2008) - [c4]Daniel Molina
Memetic Algorithm for Intense Local Search Methods Using Local Search Chains. Hybrid Metaheuristics 2008: 58-71 - [c3]Daniel Molina, Matthew Zimmerman, Gregory Roberts, Marnita Eaddie, Gilbert L. Peterson
Timely Rootkit Detection During Live Response. IFIP Int. Conf. Digital Forensics 2008: 139-148 - 2006
- [j2]Francisco Herrera
Continuous scatter search: An analysis of the integration of some combination methods and improvement strategies. Eur. J. Oper. Res. 169(2): 450-476 (2006) - [c2]Carlos García-Martínez, Manuel Lozano
A Local Genetic Algorithm for Binary-Coded Problems. PPSN 2006: 192-201 - 2005
- [c1]Daniel Molina, Francisco Herrera, Manuel Lozano:
Adaptive local search parameters for real-coded memetic algorithms. Congress on Evolutionary Computation 2005: 888-895 - 2004
- [j1]Manuel Lozano
Real-Coded Memetic Algorithms with Crossover Hill-Climbing. Evol. Comput. 12(3): 273-302 (2004)
Coauthor Index
[j35] [i10] [i9] [j34] [j33] [i8] [i7] [j32] [i6] [j31] [j30] [j29] [j28] [c30] [j26] [j25] [i5] [i4] [i3] [j24] [j23] [c26] [i1] [j22] [c23] [j21] [c22] [j20] [c21] [j18] [c20] [j15] [c19] [c18] [c17] [j14] [j13] [c16] [c15] [j9] [j8] [j7] [j6] [c11] [j5] [c10] [c9] [c8] [c7] [c6] [j4] [j3] [c4] [j2] [c1] [j1]
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last updated on 2024-11-20 21:02 CET by the dblp team
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