The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. Population-based metaheuristic methods deal in every iteration of the algorithm with a set (i.e., a population) of solutions rather than with a single solution. As they deal with a population of solutions, population-based algorithms provide a natural, intrinsic way for the exploration of the search space. Yet, the final performance depends strongly on the way the population is manipulated. The most studied population-based methods in combinatorial optimization are Evolutionary Computation (EC) and Ant Colony Optimization (ACO). In EC algorithms, a population of individuals is modified by recombination and mutation operators, and in ACO a colony of artificial ants is used to construct solutions guided by the pheromone trails and heuristic information.