Richard A. Gonçalves

646 total citations
39 papers, 381 citations indexed

About

Richard A. Gonçalves is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Richard A. Gonçalves has authored 39 papers receiving a total of 381 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 27 papers in Computational Theory and Mathematics and 5 papers in Management Science and Operations Research. Recurrent topics in Richard A. Gonçalves's work include Metaheuristic Optimization Algorithms Research (28 papers), Advanced Multi-Objective Optimization Algorithms (27 papers) and Evolutionary Algorithms and Applications (12 papers). Richard A. Gonçalves is often cited by papers focused on Metaheuristic Optimization Algorithms Research (28 papers), Advanced Multi-Objective Optimization Algorithms (27 papers) and Evolutionary Algorithms and Applications (12 papers). Richard A. Gonçalves collaborates with scholars based in Brazil, France and Spain. Richard A. Gonçalves's co-authors include Myriam Delgado, Sandra M. Venske, Carolina P. Almeida, Aurora Pozo, Ricardo Lüders, Marco C. Goldbarg, Elizabeth Ferreira Gouvêa Goldbarg, Elaine Machado Benelli, Roberto Santana and Éric Didier and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and Neurocomputing.

In The Last Decade

Richard A. Gonçalves

39 papers receiving 362 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Richard A. Gonçalves Brazil 11 227 208 71 58 45 39 381
Linjun He China 10 236 1.0× 260 1.3× 53 0.7× 79 1.4× 40 0.9× 27 469
Arturo Hernández Aguirre Mexico 11 354 1.6× 253 1.2× 43 0.6× 52 0.9× 49 1.1× 27 474
Yalan Zhou China 11 228 1.0× 181 0.9× 64 0.9× 25 0.4× 29 0.6× 24 335
Xuanxuan Ban China 8 256 1.1× 261 1.3× 45 0.6× 34 0.6× 54 1.2× 17 394
Yuanchao Liu China 14 365 1.6× 295 1.4× 47 0.7× 35 0.6× 46 1.0× 42 528
Yicun Hua China 5 249 1.1× 229 1.1× 40 0.6× 30 0.5× 48 1.1× 8 396
Haiming Lu United States 9 235 1.0× 209 1.0× 27 0.4× 39 0.7× 49 1.1× 13 395
Luka Mernik United States 5 221 1.0× 122 0.6× 37 0.5× 53 0.9× 33 0.7× 7 371
Zefeng Chen China 12 252 1.1× 230 1.1× 36 0.5× 20 0.3× 31 0.7× 28 361
Luis Miguel Antonio Mexico 8 331 1.5× 297 1.4× 25 0.4× 28 0.5× 44 1.0× 14 439

Countries citing papers authored by Richard A. Gonçalves

Since Specialization
Citations

This map shows the geographic impact of Richard A. Gonçalves'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 Richard A. Gonçalves with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard A. Gonçalves more than expected).

Fields of papers citing papers by Richard A. Gonçalves

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard A. Gonçalves. 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 Richard A. Gonçalves. The network helps show where Richard A. Gonçalves may publish in the future.

Co-authorship network of co-authors of Richard A. Gonçalves

This figure shows the co-authorship network connecting the top 25 collaborators of Richard A. Gonçalves. A scholar is included among the top collaborators of Richard A. Gonçalves 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 Richard A. Gonçalves. Richard A. Gonçalves is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Truchi, Marin, Caroline Lacoux, Julien Fassy, et al.. (2024). Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a new sparse supervised autoencoder neural network. SHILAP Revista de lepidopterología. 4. 1340339–1340339. 1 indexed citations
2.
Gonçalves, Richard A., et al.. (2020). An empirical analysis of constraint handling on evolutionary multi-objective algorithms for the Environmental/Economic Load Dispatch problem. Expert Systems with Applications. 165. 113774–113774. 42 indexed citations
3.
Delgado, Myriam, et al.. (2018). Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm. Applied Soft Computing. 73. 328–343. 5 indexed citations
4.
5.
Almeida, Carolina P., Richard A. Gonçalves, Sandra M. Venske, Ricardo Lüders, & Myriam Delgado. (2018). Multi-armed Bandit Based Hyper-Heuristics for the Permutation Flow Shop Problem. 139–144. 10 indexed citations
6.
7.
Delgado, Myriam, et al.. (2017). A Hyper-Heuristic in MOEA/D-DRA Using the Upper Confidence Bound Technique. 396–401. 4 indexed citations
8.
Gonçalves, Richard A., Carolina P. Almeida, Sandra M. Venske, Myriam Delgado, & Aurora Pozo. (2017). A New Hyper-Heuristic Based on a Restless Multi-armed Bandit for Multi-objective Optimization. 390–395. 9 indexed citations
9.
Delgado, Myriam, et al.. (2017). Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem. Journal of Heuristics. 24(1). 25–47. 9 indexed citations
10.
Delgado, Myriam, et al.. (2015). Extreme Learning Surrogate Models in Multi-objective Optimization based on Decomposition. Neurocomputing. 180. 55–67. 19 indexed citations
11.
Gonçalves, Richard A., et al.. (2015). A Hyper-Heuristic for the Environmental/Economic Dispatch Optimization Problem. 7–12. 3 indexed citations
12.
Gonçalves, Richard A., et al.. (2015). A Multi-armed Bandit Hyper-Heuristic. 3. 13–18. 6 indexed citations
13.
Gonçalves, Richard A., et al.. (2015). MOEA/D with adaptive operator selection for the environmental/economic dispatch problem. 3. 1–6. 5 indexed citations
14.
Pozo, Aurora, et al.. (2015). An Ant Colony based Hyper-Heuristic Approach for the Set Covering Problem. SHILAP Revista de lepidopterología. 4(1). 1–21. 4 indexed citations
15.
Gonçalves, Richard A., Carolina P. Almeida, Marco C. Goldbarg, Elizabeth Ferreira Gouvêa Goldbarg, & Myriam Delgado. (2013). Improved Cultural Immune Systems to solve the economic load dispatch problems. 95. 621–628. 5 indexed citations
16.
Venske, Sandra M., Richard A. Gonçalves, & Myriam Delgado. (2013). ADEMO/D: Multiobjective optimization by an adaptive differential evolution algorithm. Neurocomputing. 127. 65–77. 61 indexed citations
17.
Venske, Sandra M., Richard A. Gonçalves, & Myriam Delgado. (2012). ADEMO/D: Adaptive Differential Evolution for Multiobjective Problems. 226–231. 17 indexed citations
18.
Almeida, Carolina P., et al.. (2012). Harmony Search for Multi-objective Optimization. 220–225. 9 indexed citations
19.
Almeida, Carolina P., Richard A. Gonçalves, Elizabeth Ferreira Gouvêa Goldbarg, Marco C. Goldbarg, & Myriam Delgado. (2011). An experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem. Annals of Operations Research. 199(1). 305–341. 19 indexed citations
20.
Gonçalves, Richard A., Marco C. Goldbarg, Elizabeth Ferreira Gouvêa Goldbarg, & Myriam Delgado. (2008). Warping search. 349–350. 1 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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