---
title: "GECCO (A): papers and universities"
description: "GECCO (Genetic and Evolutionary Computations) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 504 of its papers from 2024–2026 (181 in 2024, 170 in 2025, 153 in 2026). By the countries of the authors' institutions, the most papers come from the United States (80), China (72) and the United Kingdom (70)."
url: https://scholar4ai.com/proceedings/gecco
markdown_url: https://scholar4ai.com/proceedings/gecco.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "GECCO (Genetic and Evolutionary Computations) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 504 of its papers from 2024–2026 (181 in 2024, 170 in 2025, 153 in 2026). (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)
---

# GECCO: Genetic and Evolutionary Computations — papers and institutions, 2024–2026

Papers of GECCO, Genetic and Evolutionary Computations (CORE rank A; field: Artificial intelligence), 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

- GECCO (Genetic and Evolutionary Computations) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 504 of its papers from 2024–2026 (181 in 2024, 170 in 2025, 153 in 2026).
- By the countries of the authors' institutions, the most papers come from the United States (80), China (72) and the United Kingdom (70).
- The institutions with the most papers are The University of Adelaide (25), Leiden University (19) and Southern University of Science and Technology (17).
- Its most-cited paper of these years is “LLMatic: Neural Architecture Search Via Large Language Models And Quality Diversity Optimization” (2024), with 52 citations.

## Papers per year

*GECCO papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 181 | 179 |
| 2025 | 170 | 169 |
| 2026 | 153 | 150 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4363608932](https://openalex.org/S4363608932) | The conference's proceedings |

## Who publishes there

*Institutions with the most GECCO papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | The University of Adelaide | 25 |
| 2 | [Leiden University](https://scholar4ai.com/institutions/leiden-university.md) | 19 |
| 3 | Southern University of Science and Technology | 17 |
| 4 | Victoria University of Wellington | 16 |
| 5 | [Centre National de la Recherche Scientifique](https://scholar4ai.com/institutions/centre-national-de-la-recherche-scientifique.md) | 15 |
| 6 | Universidad de Málaga | 15 |
| 7 | Yokohama National University | 14 |
| 8 | Massachusetts Institute of Technology | 13 |
| 9 | Michigan State University | 13 |
| 10 | [South China University of Technology](https://scholar4ai.com/institutions/south-china-university-of-technology.md) | 13 |

*Countries with the most GECCO papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 80 |
| 2 | China | 72 |
| 3 | United Kingdom | 70 |
| 4 | Germany | 56 |
| 5 | Australia | 51 |
| 6 | Japan | 44 |
| 7 | France | 39 |
| 8 | Netherlands | 36 |
| 9 | Spain | 33 |
| 10 | Italy | 24 |

*Keywords with the most GECCO papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | multi-objective optimization | 64 |
| 2 | evolutionary algorithms | 50 |
| 3 | combinatorial optimization | 39 |
| 4 | genetic programming | 39 |
| 5 | genetic algorithm | 33 |
| 6 | multi-objective evolutionary algorithms | 28 |
| 7 | NSGA-II | 26 |
| 8 | black-box optimization | 24 |
| 9 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 21 |
| 10 | symbolic regression | 21 |

## Most-cited papers

*Most-cited GECCO papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 52 | [LLMatic: Neural Architecture Search Via Large Language Models And Quality Diversity Optimization](https://doi.org/10.1145/3638529.3654017) | 2024-07-08 | Muhammad Umair Nasir, Sam Earle, Julian Togelius et al. |
| 2 | 40 | [Runtime Analyses of NSGA-III on Many-Objective Problems](https://doi.org/10.1145/3638529.3654218) | 2024-07-08 | Andre Opris, Duc-Cuong Dang, Frank Neumann et al. |
| 3 | 29 | [LLM Guided Evolution - The Automation of Models Advancing Models](https://doi.org/10.1145/3638529.3654178) | 2024-07-08 | Clint Morris, Michael Jurado, Jason Zutty |
| 4 | 24 | [Large-Scale Benchmarking of Metaphor-Based Optimization Heuristics](https://doi.org/10.1145/3638529.3654122) | 2024-07-08 | Diederick Vermetten, Carola Doerr, Hao Wang et al. |
| 5 | 18 | [Analysis of Real-World Constrained Multi-Objective Problems and Performance Comparison of Multi-Objective Algorithms](https://doi.org/10.1145/3638529.3653994) | 2024-07-08 | Yang Nan, Hisao Ishibuchi, Tianye Shu et al. |
| 6 | 17 | [Large Language Model-based Test Case Generation for GP Agents](https://doi.org/10.1145/3638529.3654056) | 2024-07-08 | Steven Jorgensen, Giorgia Nadizar, Gloria Pietropolli et al. |
| 7 | 16 | [Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning](https://doi.org/10.1145/3638529.3653996) | 2024-07-08 | Zeyuan Ma, Jiacheng Chen, Hongshu Guo et al. |
| 8 | 16 | [Searching for a Diversity of Interpretable Graph Control Policies](https://doi.org/10.1145/3638529.3653987) | 2024-07-08 | Giorgia Nadizar, Eric Medvet, Dennis G. Wilson |
| 9 | 15 | [Learning Traffic Signal Control via Genetic Programming](https://doi.org/10.1145/3638529.3654037) | 2024-07-08 | Xiao-Cheng Liao, Yi Mei, Mengjie Zhang |
| 10 | 13 | [Exploring the Prompt Space of Large Language Models through Evolutionary Sampling](https://doi.org/10.1145/3638529.3654049) | 2024-07-08 | Martina Saletta, Claudio Ferretti |

## Cite this page

Scholar4AI. (2026). GECCO: Genetic and Evolutionary Computations — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/gecco

```bibtex
@misc{scholar4ai-proceedings-gecco,
  author = {{Scholar4AI}},
  title = {GECCO: Genetic and Evolutionary Computations — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/gecco}},
  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/).