Conference Information

EuroGP 2027: European Conference on Genetic Programming

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Submission Date:
2026-11-01 Due in 19 days
Notification Date:
2027-01-10
Conference Date:
2027-07-31
Location:
Mainz, Germany
Years:
ICORE: B   QUALIS: B1   Viewed: 18406   Tracked: 0   Attend: 0
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EuroGP
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Conference Partner Index (CP-I)

74.8 / 100
Ranked #267 of 5,696 conferences · Top 5%
Academic recognition (35%)
78
Submission selectivity (20%)
75
Editions held (20%)
92
Community attention (10%)
12
Public record completeness (15%)
85

Inputs used: Listed as ICORE B, QUALIS B1 · Acceptance rate: 32.4% (mean of 5 editions on file) · Editions on record: 30 · Researchers who opened this page in the past 24 months: 4

Missing from the public record: Best-paper records (+2.3)
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Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-13

Call For Papers

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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Acceptance Ratio

Average acceptance rate: 35.6% over 9 years (2000–2008).

YearSubmittedAcceptedAccepted(%)
2008612134.4%
2007712129.6%
2006592135.6%
2005642031.3%
2004611931.1%
2003612439.3%
2002421842.9%
2001421740.5%
2000391435.9%

Related Journals

CCFFull NameImpact FactorPublisherISSN
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 Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203

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