会议信息
EvoCOP 2020: European Conference on Evolutionary Computation in Combinatorial Optimisation
http://www.evostar.org/2020/cfp_evocop.php
截稿日期:
2019-11-11
通知日期:
会议日期:
2020-04-15
会议地点:
Seville, Spain
届数:
20
QUALIS: b2   浏览: 10688   关注: 2   参加: 0

征稿
The 20th European Conference on Evolutionary Computation in Combinatorial Optimisation is a multidisciplinary conference that brings together researchers working on evolutionary computation methods and other metaheuristics for solving difficult combinatorial optimisation problems appearing in various industrial, economic, and scientific domains. Prominent examples of metaheuristics include: evolutionary algorithms, estimation of distribution algorithms, swarm intelligence methods such as ant colony and particle swarm optimisation, local search methods such as simulated annealing, tabu search, variable neighbourhood search, iterated local search, scatter search and path relinking, and their hybridisation, such as memetic algorithms. Automatic algorithm configuration and design, meta-optimisation, model-based methods, and hyperheuristics are also topics of interest. 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, graph problems, satisfiability, energy optimisation, cutting, packing, and planning problems.

The EvoCOP 2020 conference will be held in the city of Seville, Spain, together with EuroGP (the 23rd European Conference on Genetic Programming), EvoMUSART (the 9th European conference on evolutionary and biologically inspired music, sound, art and design) and EvoApplications (the 23rd European Conference on the Applications of Evolutionary Computation), in a joint event collectively known as EvoStar (Evo*).

Areas of Interest and Contributions

Topics of interest include, but are not limited to:

    Applications of metaheuristics to combinatorial optimisation problems
    Representation techniques
    Practical solution of NP-hard problems
    Neighbourhoods and efficient algorithms for searching them
    Variation operators for stochastic search methods
    Theoretical developments
    Constraint-handling techniques
    Parallelisation and grid computing
    Search space and landscape analyses
    Comparisons between different (also exact) methods
    Heuristics
    Genetic programming and Genetic algorithms
    Tabu search, iterated local search and variable neighbourhood search
    Ant colony optimisation
    Artificial immune systems
    Scatter search
    Particle swarm optimisation
    Memetic algorithms
    Hybrid methods and hybridisation techniques
    Matheuristics (hybrids of exact and heuristic methods)
    Hyper-heuristics and autonomous search
    Automatic algorithm configuration and design
    Metaheuristics and machine learning
    Surrogate-model-based methods
    Estimation of distribution algorithms
    String processing
    Scheduling and timetabling
    Network design
    Vehicle routing
    Graph problems
    Satisfiability
    Packing and cutting problems
    Energy optimisation problems
    Multi-objective optimisation
    Search-based software engineering

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 2019-07-14
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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