| Abstract | Metaheuristics have shown to be effective for difficult combinatorial
optimization problems appearing in various industrial, economical, and
scientific domains. Prominent examples of metaheuristics are evolutionary
algorithms, tabu search, simulated annealing, scatter search, memetic
algorithms, variable neighborhood search, iterated local search, greedy
randomized adaptive search procedures, ant colony optimization and estimation
of distribution algorithms. Problems solved successfully include scheduling,
timetabling, network design, transportation and distribution, vehicle routing,
the travelling salesman problem, packing and cutting, satisfiability and
general mixed integer programming.
EvoCOP began in 2001 and has been held annually since then. It is
the first event specifically dedicated to the application of
evolutionary computation and related methods to combinatorial
optimization problems. Originally held as a workshop, EvoCOP became
a conference in 2004. The events gave researchers an excellent
opportunity to present their latest research and to discuss current
developments and applications. Following
the general trend of hybrid metaheuristics and diminishing
boundaries between the different classes of metaheuristics, EvoCOP
has broadened its scope over the last years and invited submissions
on any kind of metaheuristic for combinatorial optimization.
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