Conference Information

EvoCOP 2027: European Conference on Evolutionary Computation in Combinatorial Optimisation

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Submission Date:
2026-11-01 Due in 8 days
Notification Date:
2027-01-10
Conference Date:
2027-03-31
Location:
Mainz, Germany
Years:
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ICORE: B   QUALIS: B2   Viewed: 23440   Tracked: 2   Attend: 0
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Conference Partner Index (CP-I)

72.4 / 100
Ranked #302 of 5,759 conferences · Top 6%

#21 of 142 in Theory & Algorithms #26 of 751 in Artificial Intelligence & Machine Learning

Academic recognition (35%)
72
Submission selectivity (20%)
69
Editions held (20%)
90
Community attention (10%)
27
Public record completeness (15%)
85

Inputs used: Listed as ICORE B, QUALIS B2 · Acceptance rate: 37.6% (mean of 5 editions on file) · Editions on record: 27 · Researchers following it here: 2 · Researchers who opened this page in the past 24 months: 8

Missing from the public record: 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 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 1.2 · Computed 2026-10-24

Call For Papers

EvoCOP 2027 (European Conference on Evolutionary Computation in Combinatorial Optimisation) is a ICORE B / QUALIS B2 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.

The 27th European Conference on Evolutionary Computation in Combinatorial Optimisation is a multidisciplinary conference that brings together researchers working on applications and theory of evolutionary computation methods and other metaheuristics for solving difficult combinatorial optimisation problems appearing in various industrial, economic, and scientific domains. Successfully solved problems include, but are not limited to, multi-objective, uncertain, dynamic and stochastic problems in the context of scheduling, timetabling, network design, transportation and distribution, vehicle routing, stringology, graphs, satisfiability, energy optimisation, cutting, packing, planning, and search-based software engineering. Successfully addressed theoretical and translational challenges encompass, but are not limited to, the development and analysis of novel (components of) evolutionary and other metaheuristic algorithms, work on neighbourhood and landscape structures, problem-agnostic and problem-specific variation operators, parallelisation strategies, and hybridizations. The EvoCOP 2027 conference will be held together with EuroGP (the 30th European Conference on Genetic Programming), EvoMUSART (the 16th European conference on evolutionary and biologically inspired music, sound, art and design), EvoApplications (the 30th European Conference on the Applications of Evolutionary Computation), and EvoLearn (the 1st International Conference on Evolutionary Computation and Learning), in a joint event collectively known as EvoStar (Evo*). Accepted papers will be published by Springer Nature in the Lecture Notes in Computer Science series. Previous proceedings can be found in the EvoCOP Conference Proceedings in SpringerLink. Areas of Interest and Contributions EvoCOP welcomes submissions in all experimental and theoretical aspects of evolutionary computation and other metaheuristics to combinatorial optimisation problems, including (but not limited to) the following areas: Applications of metaheuristics to combinatorial optimization problems Theoretical developments Neighbourhoods and efficient algorithms for searching them Variation operators for stochastic search methods Constraint-handling techniques Parallelisation and grid computing Search space and landscape analyses Comparisons between different (also exact) methods Automatic algorithm configuration and design Prominent examples of metaheuristics include (but are not limited to): Evolutionary algorithms Estimation of distribution algorithms Swarm intelligence methods such as ant colony and particle swarm optimisation Artificial immune systems Local search methods such as simulated annealing, tabu search, variable neighbourhood search, iterated local search, scatter search and path relinking Hybrid methods such as memetic algorithms Matheuristics (hybrids of exact and heuristic methods) Hyper-heuristics and autonomous search Surrogate-model-based methods Notice that, by tradition, continuous/numerical optimisation is *not* part of the topics of interest of EvoCOP. Interested authors might consider submitting to other EvoStar conferences such as EvoApplications.
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Acceptance Ratio

Average acceptance rate: 41.6% over 21 years (2001–2022).

YearSubmittedAcceptedAccepted(%)
2022281346.4%
2021421433.3%
2020371437.8%
2019371437.8%
2018371232.4%
2017391641%
2016441738.6%
2015461941.3%
2014422047.6%
2013502346%
2012482245.8%
2011422252.4%
2009532139.6%
2008692434.8%
2007812125.9%
2006772431.2%
2005662436.4%
2004862326.7%
2003391948.7%
2002321856.3%
2001312374.2%

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CCFFull NameImpact FactorPublisherISSN
Swarm and Evolutionary Computation8.5Elsevier2210-6502
BEvolutionary Computation3.4MIT Press1063-6560
Journal of Combinatorial Optimization1.1Springer1382-6905
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

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