会议信息
EvoCOP 2025: European Conference on Evolutionary Computation in Combinatorial Optimisation
https://www.evostar.org/2025/evocop/
截稿日期:
2024-11-01
通知日期:
2025-01-10
会议日期:
2025-04-23
会议地点:
Trieste, Italy
届数:
25
QUALIS: b2   浏览: 12299   关注: 2   参加: 0

征稿
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.
最后更新 Dou Sun 在 2024-10-07
录取率
时间提交数录取数录取率(%)
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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