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. Also as Markdown.

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@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}
}