Daniel Molina Cabrera
Daniel Molina Cabrera
Computer Science, Granada University
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Cited by
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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
AB Arrieta, N Díaz-Rodríguez, J Del Ser, A Bennetot, S Tabik, A Barbado, ...
Information fusion 58, 82-115, 2020
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
J Derrac, S García, D Molina, F Herrera
Swarm and Evolutionary Computation 1 (1), 3-18, 2011
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
Bio-inspired computation: Where we stand and what's next
J Del Ser, E Osaba, D Molina, XS Yang, S Salcedo-Sanz, D Camacho, ...
Swarm and Evolutionary Computation 48, 220-250, 2019
Real-coded memetic algorithms with crossover hill-climbing
M Lozano, F Herrera, N Krasnogor, D Molina
Evolutionary computation 12 (3), 273-302, 2004
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
A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems
E Osaba, E Villar-Rodriguez, J Del Ser, AJ Nebro, D Molina, A LaTorre, ...
Swarm and Evolutionary Computation 64, 100888, 2021
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
Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations
D Molina, J Poyatos, JD Ser, S García, A Hussain, F Herrera
Cognitive Computation 12, 897-939, 2020
MA-SW-Chains: Memetic algorithm based on local search chains for large scale continuous global optimization
D Molina, M Lozano, F Herrera
IEEE congress on evolutionary computation, 1-8, 2010
Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
M Lozano, D Molina, F Herrera
Soft computing 15, 2085-2087, 2011
A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: Progress and prospects
I Palomares, E Martínez-Cámara, R Montes, P García-Moral, M Chiachio, ...
Applied Intelligence 51, 6497-6527, 2021
Adaptive local search parameters for real-coded memetic algorithms
D Molina, F Herrera, M Lozano
2005 IEEE Congress on Evolutionary Computation 1, 888-895, 2005
Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems
F Herrera, M Lozano, D Molina
Last accessed: July, 2010
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
Memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains
D Molina, M Lozano, AM Sánchez, F Herrera
Soft Computing 15, 2201-2220, 2011
An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions
D Molina, A LaTorre, F Herrera
Cognitive Computation 10, 517-544, 2018
SHADE with iterative local search for large-scale global optimization
D Molina, A LaTorre, F Herrera
2018 IEEE congress on evolutionary computation (CEC), 1-8, 2018
A walk into metaheuristics for engineering optimization: principles, methods and recent trends
N Xiong, D Molina, ML Ortiz, F Herrera
international journal of computational intelligence systems 8 (4), 606-636, 2015
A prescription of methodological guidelines for comparing bio-inspired optimization algorithms
A LaTorre, D Molina, E Osaba, J Poyatos, J Del Ser, F Herrera
Swarm and Evolutionary Computation 67, 100973, 2021
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