Evolutionary Computation in Combinatorial Optimization
Metaheuristics have often been shown to be e?ective for di?cult combinatorial optimization problems appearing in various industrial, economical, and scienti?c 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 problems, vehicle routing, the traveling sal- person problem, satis?ability, packing and cutting problems, planning problems, and general mixed integer programming. The EvoCOP event series started in 2001 and has been held annually since then. It was the ?rst speci?cally dedicated to the application of evolutionary computation and related methods to combinatorial optimization problems. E- lutionary computation involves the study of problem-solving and optimization techniques inspired by principles of natural evolution and genetics. Following the general trend of hybrid metaheuristics and diminishing boundaries between the di?erent classes of metaheuristics, EvoCOP has broadened its scope over the lastyearsandinvitedsubmissionsonanykindofmetaheuristicforcombinatorial optimization problems.