| Abstract | Metaheuristics have often been shown to be effective for difficult combinatorial
optimization problems appearing in various industrial, economical, and scientific
domains. Prominent examples of metaheuristics are evolutionary algorithms,
simulated annealing, tabu search, scatter search, memetic algorithms, variable
neighborhood search, iterated local search, greedy randomized adaptive search
procedures, estimation of distribution algorithms, and ant colony optimization.
Successfully solved problems include scheduling, timetabling, network design,
transportation and distribution, vehicle routing, the traveling salesman problem,
satisfiability, packing and cutting, and general mixed integer programming.
EvoCOP began in 2001 and has been held annually since then. It was 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 as well as providing for improved interaction
between members of this scientific community. 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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