Evolutionary Computation in Combinatorial Optimization

Metaheuristics have been shown to be e?ective for di?cult combinatorial op- mization problems appearing in a wide variety of industrial, economic, and sci- ti?c 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, satis?ability, and general mixed integer programming. EvoCOP began in 2001 and has been held annually since then. It is the ?rst event speci?cally 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 - velopments and applications. Following the general trend of hybrid metaheur- tics and diminishing boundaries between the di?erent classes of metaheuristics, EvoCOP has broadened its scope in recent years and invited submissions on any kind of metaheuristic for combinatorial optimization.