Daniel Molina Cabrera

Senior Lecturer, Assistant Professor

Universidad de Granada

Spain

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About

Daniel Molina Cabrera serves as a Senior Lecturer and Assistant Professor at the University of Granada, Spain. His research interests include evolutionary computation, bio-inspired optimization, and artificial intelligence, with a focus on enhancing the design and performance of neural networks. He has contributed to recent literature addressing critical issues in optimization methodologies and their applications toward sustainable development goals.

Articles (12)

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2025

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2025

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2023

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2023

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2022

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2022

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2021

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2021

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2021

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2021

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SPAIN

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Francisco Javier Melero Rus

Universidad de Granada

SPAIN

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Sergio Alonso Burgos

Universidad de Granada

SPAIN

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Daniel Molina Cabrera

Research Interests

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Articles (12)

The paradox of success in evolutionary and bioinspired optimization: Revisiting critical issues, key studies, and methodological pathways

Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects

Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness

EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks

NATURE- AND BIO-INSPIRED OPTIMIZATION: THE GOOD, THE BAD, THE UGLY AND THE HOPEFUL

A least square support vector machine approach based on bvRNA-GA for modeling photovoltaic systems

Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems

Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges

Performance analysis of real-coded evolutionary algorithms under a computationally expensive optimization scenario: 3D–2D Comparative Radiography

Collaborators (7)

Esther Villar Rodríguez

Antonio LaTorre

Manuel Chiachio Ruano

JUAN CHIACHIO-RUANO

Óscar David Gómez López

Francisco Javier Melero Rus

Sergio Alonso Burgos

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