Gara Miranda

825 total citations
54 papers, 459 citations indexed

About

Gara Miranda is a scholar working on Industrial and Manufacturing Engineering, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Gara Miranda has authored 54 papers receiving a total of 459 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Industrial and Manufacturing Engineering, 16 papers in Artificial Intelligence and 16 papers in Computational Theory and Mathematics. Recurrent topics in Gara Miranda's work include Advanced Multi-Objective Optimization Algorithms (16 papers), Optimization and Packing Problems (14 papers) and Metaheuristic Optimization Algorithms Research (14 papers). Gara Miranda is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (16 papers), Optimization and Packing Problems (14 papers) and Metaheuristic Optimization Algorithms Research (14 papers). Gara Miranda collaborates with scholars based in Spain, Mexico and Egypt. Gara Miranda's co-authors include Coromoto León, Carlos Segura, Carlos A. Coello Coello, Eduardo Segredo, Jésica de Armas, C. Rodrı́guez, Juan A. Gómez‐Pulido, Miguel A. Vega‐Rodríguez, Enrique Alba and Eulogio Pleguezuelos and has published in prestigious journals such as European Journal of Operational Research, The Journal of Urology and IEEE Access.

In The Last Decade

Gara Miranda

49 papers receiving 446 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Gara Miranda Spain	12	192	175	121	61	55	54	459
Hong Choon Ong Malaysia	9	169 0.9×	66 0.4×	31 0.3×	31 0.5×	24 0.4×	20	310
Luka Mernik United States	5	221 1.2×	122 0.7×	37 0.3×	53 0.9×	18 0.3×	7	371
Linjun He China	10	236 1.2×	260 1.5×	53 0.4×	79 1.3×	23 0.4×	27	469
Yuren Zhou China	11	515 2.7×	453 2.6×	96 0.8×	33 0.5×	36 0.7×	18	694
Morteza Alinia Ahandani Iran	11	227 1.2×	110 0.6×	46 0.4×	59 1.0×	19 0.3×	25	367
Binping Zhao China	5	190 1.0×	79 0.5×	42 0.3×	46 0.8×	49 0.9×	6	356
Changchun Pan China	9	114 0.6×	35 0.2×	95 0.8×	31 0.5×	14 0.3×	37	310
Dongdong Yang China	11	310 1.6×	182 1.0×	34 0.3×	60 1.0×	21 0.4×	23	484
Lie Meng Pang China	9	277 1.4×	267 1.5×	59 0.5×	42 0.7×	17 0.3×	46	497

Countries citing papers authored by Gara Miranda

Since Specialization

Citations

This map shows the geographic impact of Gara Miranda's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gara Miranda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gara Miranda more than expected).

Fields of papers citing papers by Gara Miranda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gara Miranda. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gara Miranda. The network helps show where Gara Miranda may publish in the future.

Co-authorship network of co-authors of Gara Miranda

This figure shows the co-authorship network connecting the top 25 collaborators of Gara Miranda. A scholar is included among the top collaborators of Gara Miranda based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gara Miranda. Gara Miranda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Segura, Carlos, et al.. (2025). Future trends in the design of memetic algorithms: the case of the linear ordering problem. Neural Computing and Applications. 37(18). 12471–12485.

León, Coromoto, et al.. (2024). Training future engineers: Integrating Computational Thinking and effective learning methodologies into education. Computer Applications in Engineering Education. 32(3). 5 indexed citations

Arnay, Rafael, et al.. (2024). Using RoblockLLy in the Classroom: Bridging the Gap in Computer Science Education Through Robotics Simulation. IEEE Transactions on Learning Technologies. 18. 39–52.

León, Coromoto, et al.. (2023). What Emotions do Pre-university Students Feel when Engaged in Computational Thinking Activities?. Edinburgh Napier Research Repository (Edinburgh Napier University). 6(2). 2 indexed citations

Miranda, Gara, et al.. (2022). Engaging Primary and Secondary School Students in Computer Science Through Computational Thinking Training. IEEE Transactions on Emerging Topics in Computing. 11(1). 56–69. 10 indexed citations

Miranda, Gara, et al.. (2020). Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem. Mathematics. 8(11). 2018–2018. 26 indexed citations

Simone, Giuseppe, Umberto Anceschi, Aldo Brassetti, et al.. (2019). MP38-17 VALIDATION OF THE COBRA NOMOGRAM FOR THE PREDICTION OF CANCER SPECIFIC SURVIVAL IN PATIENTS WITH BLADDER CANCER TREATED WITH RADICAL CYSTECTOMY. The Journal of Urology. 201(Supplement 4). 1 indexed citations

Segredo, Eduardo, et al.. (2019). El proyecto Piens@ Computacion@LLmente. Zaguan (Universidad de Zaragoza). 573–578. 4 indexed citations

Miranda, Gara, et al.. (2016). Multi-objective Multi-level Filling Evolutionary Algorithm for the 3D Cutting Stock Problem. Procedia Computer Science. 96. 355–364. 6 indexed citations

10.

Segura, Carlos, Carlos A. Coello Coello, Gara Miranda, & Coromoto León. (2015). Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization. Annals of Operations Research. 240(1). 217–250. 65 indexed citations

11.

Segura, Carlos, Carlos A. Coello Coello, Gara Miranda, & Coromoto León. (2013). Using multi-objective evolutionary algorithms for single-objective optimization. 4OR. 11(3). 201–228. 46 indexed citations

12.

Armas, Jésica de, Gara Miranda, & Coromoto León. (2011). Hyperheuristic encoding scheme for multi-objective guillotine cutting problems. 1683–1690. 7 indexed citations

13.

Rodríguez, S., et al.. (2011). Simulation of impulse response for indoor wireless optical channels using 3D CAD models. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8067. 80670A–80670A. 1 indexed citations

14.

Miranda, Gara, Jésica de Armas, Carlos Segura, & Coromoto León. (2010). Hyperheuristic codification for the multi-objective 2D Guillotine Strip Packing Problem. 1–8. 7 indexed citations

15.

Armas, Jésica de, Coromoto León, & Gara Miranda. (2010). Fine and Coarse-grained Parallel Algorithms for the 2D Cutting Stock Problem. 4128. 255–262. 1 indexed citations

16.

Maio, Francesco Di, et al.. (2009). [Surgical management of schwannoma of biliary tract].. PubMed. 61(1). 119–21. 6 indexed citations

17.

León, Coromoto, Gara Miranda, & Carlos Segura. (2008). Optimizing the Configuration of a Broadcast Protocol through Parallel Cooperation of Multi-objective Evolutionary Algorithms. 135–140. 5 indexed citations

18.

León, Coromoto, et al.. (2007). A Parallel Skeleton for the Strength Pareto Evolutionary Algorithm 2. 434–441. 1 indexed citations

19.

León, Coromoto, et al.. (2006). Two-Dimensional Cutting Stock Problem: Shared Memory Parallelizations. 438–443. 1 indexed citations

20.

Fernández, A., et al.. (1989). Síndrome de Turcot: Dos nuevos casos en hermanos. Anales de Pediatría. 30(3). 223–224. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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