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D. Molina

4 papers hereh-index 2819.6k citations75 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.NE3
  • cs.AI1
same name
  • D. Molina — 13 papers, h 22
  • D. Molina — 3 papers
  • D. Molina — 2 papers, h 3
  • D. Molina — 1 paper, h 18
  • D. Molina — 1 paper
  • D. Molina — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192025
most citedA Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems

407 citations · 447 across the 4 of their papers we have counts for

collaborators
Showing cs.NEShow all

3 papers · 1 filter

cs.NE2025★ 1 cited

The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways

Daniel Molina, Javier Del Ser, Javier Poyatos +1

Evolutionary and bioinspired computation are crucial for efficiently addressing complex optimization problems across diverse application domains. By mimicking processes observed in…

cs.NE2024★ 407 cited

A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems

Eneko Osaba, Esther Villar-Rodriguez, Javier Del Ser +6

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research st…

cs.NE2020

Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges

Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez +5

Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyper-parame…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.