José-María Peña

1.2k total citations
34 papers, 830 citations indexed

About

José-María Peña is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, José-María Peña has authored 34 papers receiving a total of 830 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 12 papers in Computational Theory and Mathematics and 4 papers in Molecular Biology. Recurrent topics in José-María Peña's work include Metaheuristic Optimization Algorithms Research (14 papers), Evolutionary Algorithms and Applications (13 papers) and Advanced Multi-Objective Optimization Algorithms (12 papers). José-María Peña is often cited by papers focused on Metaheuristic Optimization Algorithms Research (14 papers), Evolutionary Algorithms and Applications (13 papers) and Advanced Multi-Objective Optimization Algorithms (12 papers). José-María Peña collaborates with scholars based in Spain, United Kingdom and France. José-María Peña's co-authors include Antonio LaTorre, Santiago Muelas, Antoine Jérusalem, Jayaratnam Jayamohan, Ruth E. Baker, Alexander Mendiburu, Jesús A. Garcı́a, Riyi Shi, Alex A. Freitas and Jesús Montes and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and AIAA Journal.

In The Last Decade

José-María Peña

31 papers receiving 813 citations

Peers

José-María Peña

CTM

IME

Ulrich Hammel Germany

Marco Castellani United Kingdom

Soraya Rana United States

Zunlei Feng China

Terence Soule United States

Vinícius Veloso de Melo Brazil

Gianluigi Folino Italy

Helong Yu China

Xuming Han China

Tomoharu Nagao Japan

Ulrich Hammel Germany

José-María Peña

611 ×1.4

331 ×1.0

CTM

57 ×0.6

70 ×1.0

IME

15 ×0.2

Citations per year, relative to José-María Peña José-María Peña (= 1×) peers Ulrich Hammel

Countries citing papers authored by José-María Peña

Since Specialization

Citations

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

Fields of papers citing papers by José-María Peña

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by José-María Peña. 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 José-María Peña. The network helps show where José-María Peña may publish in the future.

Co-authorship network of co-authors of José-María Peña

This figure shows the co-authorship network connecting the top 25 collaborators of José-María Peña. A scholar is included among the top collaborators of José-María Peña 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 José-María Peña. José-María Peña 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

Wei, Yuyang, et al.. (2025). A mechanics-informed machine learning framework for traumatic brain injury prediction in police and forensic investigations. Communications Engineering. 4(1). 29–29.

Nguyen, Van Dung, et al.. (2024). Soft-Finite-Element-Method-Driven Multiphysics Airfoil Optimization. AIAA Journal. 63(1). 148–161.

Voets, Natalie, et al.. (2021). A Machine Learning Enhanced Mechanistic Simulation Framework for Functional Deficit Prediction in TBI. Frontiers in Bioengineering and Biotechnology. 9. 587082–587082. 10 indexed citations

Peña, José-María, et al.. (2021). Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter. Computational Mechanics. 67(6). 1629–1643. 11 indexed citations

Baker, Ruth E., José-María Peña, Jayaratnam Jayamohan, & Antoine Jérusalem. (2018). Mechanistic models versus machine learning, a fight worth fighting for the biological community?. Biology Letters. 14(5). 20170660–20170660. 232 indexed citations

LaTorre, Antonio & José-María Peña. (2017). On the scalability of population restart mechanisms on large-scale global optimization. 1. 1071–1078. 2 indexed citations

Peña, José-María, et al.. (2015). Neurite, a Finite Difference Large Scale Parallel Program for the Simulation of Electrical Signal Propagation in Neurites under Mechanical Loading. PLoS ONE. 10(2). e0116532–e0116532. 18 indexed citations

Muelas, Santiago, Antonio LaTorre, & José-María Peña. (2015). A distributed VNS algorithm for optimizing dial-a-ride problems in large-scale scenarios. Transportation Research Part C Emerging Technologies. 54. 110–130. 33 indexed citations

Montes, Jesús, et al.. (2013). A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses. PLoS ONE. 8(7). e68888–e68888. 3 indexed citations

10.

LaTorre, Antonio, Santiago Muelas, & José-María Peña. (2013). Large scale global optimization: Experimental results with MOS-based hybrid algorithms. 2742–2749. 95 indexed citations

11.

Muelas, Santiago, Alexander Mendiburu, Antonio LaTorre, & José-María Peña. (2013). Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior?. Information Sciences. 268. 231–254. 15 indexed citations

12.

Muelas, Santiago, Antonio LaTorre, & José-María Peña. (2013). A variable neighborhood search algorithm for the optimization of a dial-a-ride problem in a large city. Expert Systems with Applications. 40(14). 5516–5531. 31 indexed citations

13.

Garcı́a, Jesús A., et al.. (2012). A Model of the Spatially Dependent Mechanical Properties of the Axon During Its Growth. Computer Modeling in Engineering & Sciences. 87(5). 411–432. 12 indexed citations

14.

Jiménez-Montero, J. A., Óscar González-Recio, R. Alenda, & José-María Peña. (2012). Genomic evaluation using machine learning algorithms in the Spanish Holstein population. Bulletin - International Bull Evaluation Service/Interbull bulletin. 66–71. 2 indexed citations

15.

LaTorre, Antonio, Santiago Muelas, & José-María Peña. (2012). Multiple Offspring Sampling in Large Scale Global Optimization. 49 indexed citations

16.

Muelas, Santiago, Antonio LaTorre, & José-María Peña. (2012). A new methodology for the automatic creation of adaptive hybrid algorithms. Intelligent Data Analysis. 16(1). 3–23. 2 indexed citations

17.

Muelas, Santiago, Alexander Mendiburu, Antonio LaTorre, & José-María Peña. (2011). WITHDRAWN: Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior?. Information Sciences. 1 indexed citations

18.

LaTorre, Antonio, Santiago Muelas, & José-María Peña. (2011). Benchmarking a hybrid DE-RHC algorithm on real world problems. 1027–1033. 22 indexed citations

19.

Pena, Luís, José-María Peña, Sascha Ossowskí, & Pilar Herrero. (2010). Evolving Q-learners for stochastic games: Study on video game agent controllers. World Automation Congress. 1–6. 1 indexed citations

20.

Muelas, Santiago, José-María Peña, Antonio LaTorre, & Vı́ctor Robles. (2010). A new initialization procedure for the distributed estimation of distribution algorithms. Soft Computing. 15(4). 713–720. 5 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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