--- title: "PPSN (A): papers and universities" description: "PPSN (Parallel Problem Solving from Nature) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 266 of its papers from 2024–2026 (101 in 2024, 165 in 2026; 2025 not in OpenAlex yet). By the countries of the authors' institutions, the most papers come from China (60), France (41) and Germany (35)." url: https://scholar4ai.com/proceedings/ppsn markdown_url: https://scholar4ai.com/proceedings/ppsn.md license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/) share_text: "PPSN (Parallel Problem Solving from Nature) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 266 of its papers from 2024–2026 (101 in 2024, 165 in 2026; 2025 not in OpenAlex yet). (as of 2026-11-02)" data_url: https://scholar4ai.com/data/rankings.json updated: 2026-11-02T12:20:52+09:00 window: 2026-10-04 to 2026-11-02 source: OpenAlex (CC0) --- # PPSN: Parallel Problem Solving from Nature — papers and institutions, 2024–2026 Papers of PPSN, Parallel Problem Solving from Nature (CORE rank A; field: Machine learning), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02. All conferences: https://scholar4ai.com/proceedings.md ## Key findings - PPSN (Parallel Problem Solving from Nature) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 266 of its papers from 2024–2026 (101 in 2024, 165 in 2026; 2025 not in OpenAlex yet). - By the countries of the authors' institutions, the most papers come from China (60), France (41) and Germany (35). - The institutions with the most papers are Centre National de la Recherche Scientifique (27), Southern University of Science and Technology (15) and The University of Adelaide (15). - Its most-cited paper of these years is “A First Running Time Analysis of the Strength Pareto Evolutionary Algorithm 2 (SPEA2)” (2024), with 19 citations. ## Papers per year *PPSN papers per publication year in OpenAlex (–: not in OpenAlex yet)* | Year | Papers | With affiliations | |---|---:|---:| | 2024 | 101 | 101 | | 2025 | – | – | | 2026 | 165 | 164 | ## Sources *Where OpenAlex lists the papers* | OpenAlex source | Which papers | |---|---| | [S106296714](https://openalex.org/S106296714) | Papers of the volumes titled “Parallel Problem Solving from Nature – PPSN” (LNCS, every other year), found by DOI through Crossref | ## Who publishes there *Institutions with the most PPSN papers, 2024–2026* | # | Institution | Papers | |---:|---|---:| | 1 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 27 | | 2 | Southern University of Science and Technology | 15 | | 3 | The University of Adelaide | 15 | | 4 | Institut national de recherche en sciences et technologies du numérique | 11 | | 5 | École Polytechnique | 10 | | 6 | Yokohama National University | 10 | | 7 | Université de Lille | 9 | | 8 | ETH Zurich | 9 | | 9 | Sorbonne Université | 9 | | 10 | Centre de Recherche en Informatique, Signal et Automatique de Lille | 9 | *Countries with the most PPSN papers, 2024–2026* | # | Country | Papers | |---:|---|---:| | 1 | China | 60 | | 2 | France | 41 | | 3 | Germany | 35 | | 4 | United Kingdom | 33 | | 5 | United States | 28 | | 6 | Netherlands | 26 | | 7 | Australia | 18 | | 8 | Japan | 16 | | 9 | Poland | 11 | | 10 | Switzerland | 10 | *Keywords with the most PPSN papers, 2024–2026* | # | Keyword | Papers | |---:|---|---:| | 1 | evolutionary algorithms | 31 | | 2 | multi-objective optimization | 31 | | 3 | runtime analysis | 29 | | 4 | combinatorial optimization | 27 | | 5 | multi-objective evolutionary algorithms | 24 | | 6 | black-box optimization | 20 | | 7 | CMA-ES | 18 | | 8 | NSGA-II | 16 | | 9 | genetic programming | 16 | | 10 | symbolic regression | 16 | ## Most-cited papers *Most-cited PPSN papers of 2024–2026 (citations counted by OpenAlex)* | # | Citations | Title | Published | Authors | |---:|---:|---|---|---| | 1 | 19 | [A First Running Time Analysis of the Strength Pareto Evolutionary Algorithm 2 (SPEA2)](https://doi.org/10.1007/978-3-031-70071-2_19) | 2024-01-01 | Shengjie Ren, Chao Bian, Miqing Li et al. | | 2 | 18 | [Automatic Brain Tumor Segmentation Using Convolutional Neural Networks: U-Net Framework with PSO-Tuned Hyperparameters](https://doi.org/10.1007/978-3-031-70071-2_21) | 2024-01-01 | Shoffan Saifullah, Rafał Dreżewski | | 3 | 14 | [Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Models](https://doi.org/10.1007/978-3-031-70068-2_12) | 2024-01-01 | Rui Zhang, Fei Liu, Xi Lin et al. | | 4 | 14 | [Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms](https://doi.org/10.1007/978-3-031-70085-9_10) | 2024-01-01 | Simon Wietheger, Benjamin Doerr | | 5 | 12 | [Multimodal Adaptive Graph Evolution for Program Synthesis](https://doi.org/10.1007/978-3-031-70055-2_19) | 2024-01-01 | Camilo De La Torre, Yuri Cossich Lavinas, Kévin Cortacero et al. | | 6 | 12 | [Evolve Cost-Aware Acquisition Functions Using Large Language Models](https://doi.org/10.1007/978-3-031-70068-2_23) | 2024-01-01 | Yiming Yao, Fei Liu, Cheng Ji et al. | | 7 | 9 | [The Inefficiency of Genetic Programming for Symbolic Regression](https://doi.org/10.1007/978-3-031-70055-2_17) | 2024-01-01 | Gabriel Kronberger, Fabrício Olivetti de França, Harry Desmond et al. | | 8 | 8 | [LB+IC-CMA-ES: Two Simple Modifications of CMA-ES to Handle Mixed-Integer Problems](https://doi.org/10.1007/978-3-031-70068-2_18) | 2024-01-01 | Tristan Marty, Nikolaus Hansen, Anne Auger et al. | | 9 | 7 | [Avoiding Redundant Restarts in Multimodal Global Optimization](https://doi.org/10.1007/978-3-031-70068-2_17) | 2024-01-01 | Jacob de Nobel, Diederick Vermetten, Anna V. Kononova et al. | | 10 | 7 | [Improving the Performance of Relocation Rules for the Container Relocation Problem with the Rollout Algorithm](https://doi.org/10.1007/978-3-031-70055-2_12) | 2024-01-01 | Marko Ðurasević, Mateja Đumić, Francisco Javier Gil-Gala et al. | ## Cite this page Scholar4AI. (2026). PPSN: Parallel Problem Solving from Nature — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/ppsn ```bibtex @misc{scholar4ai-proceedings-ppsn, author = {{Scholar4AI}}, title = {PPSN: Parallel Problem Solving from Nature — papers and institutions, 2024–2026}, year = {2026}, howpublished = {\url{https://scholar4ai.com/proceedings/ppsn}}, urldate = {2026-11-02}, note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0} } ``` License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).