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EuroGP 2027: European Conference on Genetic Programming

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截稿日期:
2026-11-01 已截稿
通知日期:
2027-01-10
会议日期:
2027-07-31
会议地点:
Mainz, Germany
届数:
ICORE: B   QUALIS: B1   浏览: 18770   关注: 0   参加: 0
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会伴指数 (CP-I)

74.8 / 100
全站第 271 名 / 共 5,769 个会议 · 前 5%
学术认可 (35%)
78
投稿选择性 (20%)
75
会议传承 (20%)
92
社区关注 (10%)
12
资料公开度 (15%)
85

用到的输入: 收录等级：ICORE B, QUALIS B1 · 录用率：32.4%（有记录的 5 届的均值） · 有据可查的届次：30 · 过去 24 个月打开过本页的研究者：4 人

公开资料里还缺: 最佳论文记录 (+2.3)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-11-05

征稿

EuroGP 2027 (European Conference on Genetic Programming) is a ICORE B / QUALIS B1 conference held in Mainz, Germany on 2027-07-31. The paper submission deadline is 2026-11-01. Acceptance notifications are sent on 2027-01-10.

Areas of Interest and Contributions Topics to be covered include, but is not limited to: Methodological advances: Algorithms, representations, and operators for GP Surrogate models and fitness approximation in GP Explainability and interpretability in GP GP representations: tree-based, linear, graph-based, grammar-based Evolutionary design Multiple populations, coevolution, and modularity in GP Multi-objective GP Theoretical developments Infrastructure and Evaluation Methodologies: Benchmarking frameworks and reproducibility in GP Scalable, parallel, and distributed GP systems GP in embedded and resource-constrained environments Tools and techniques for visualization and analysis tools of GP systems Meta-evolution and Self-adaptation: Self-adaptive and self-configuring GP AutoML and GP-based pipeline optimization Meta-learning and evolution of GP parameters and operators Applications: GP for scientific discovery and symbolic modeling GP for continuous control and evolutionary robotics Real-world applications of GP in medicine, finance, etc. Evolvable hardware GP for data privacy, security, and adversarial robustness Genetic improvement and GP for software engineering Program synthesis and inductive programming via GP Hybrid and Unconventional Approaches: Hybridization of GP with other machine learning methods Neuroevolution and neural-GP hybrids Integration of GP with swarm intelligence or other evolutionary paradigms Unconventional GP paradigms Open-ended GP systems
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录用率

平均录用率: 35.6% 9 年间 (2000–2008).

年份提交数录用数录用率(%)
2008612134.4%
2007712129.6%
2006592135.6%
2005642031.3%
2004611931.1%
2003612439.3%
2002421842.9%
2001421740.5%
2000391435.9%

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Genetic Programming and Evolvable Machines0.9Springer1389-2576
Programming and Computer Software0.5Springer0361-7688
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.5Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203

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