The 26th 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 2026 conference will be held together with EuroGP (the 29th European Conference on Genetic Programming), EvoMUSART (the 15th European conference on evolutionary and biologically inspired music, sound, art and design) and EvoApplications (the 29th European Conference on the Applications of Evolutionary Computation), 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.