Conference Information

EvoLearn 2027: International Conference on Evolutionary Computation and Learning

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Submission Date:
2026-11-01 Due in 44 days
Notification Date:
2027-01-10
Conference Date:
2027-03-31
Location:
Mainz, Germany
Years:
1
Viewed: 549   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

37.3 / 100
Ranked #5,546 of 5,682 conferences · Top 98%

#714 of 739 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50
Submission selectivity (20%) No data - scored at the neutral baseline of 50
Editions held (20%)
19
Community attention (10%)
8
Public record completeness (15%)
35

Inputs used: Editions on record: 1 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - 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 2026-09-18

Call For Papers

EvoLearn 2027 (International Conference on Evolutionary Computation and Learning) is an academic conference held in Mainz, Germany on 2027-03-31. The paper submission deadline is 2026-11-01. Acceptance notifications are sent on 2027-01-10.

Areas of Interest and Contributions EvoLearn is particularly interested in, but not limited to, original theoretical and/or experimental works combining one way or another Evolutionary Computation and Machine Learning. Some examples are: EC for fine-tuning of ML models, Evolutionary Reinforcement Learning, Evolutionary prompt and pre-prompt optimization, Evolutionary Neural Architecture search EC or ML for metaheuristic algorithm selection and configuration, ML for evolutionary variation operators, Representation learning, Surrogate modeling, Linkage learning. Any intricate joint work of EC and ML Beyond such recombinations, EvoLearn also welcomes contributions to theory, methodology or application of Evolutionary Computation from the point of view of learning at the population level.
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