Evolutionary algorithms for dynamic optimization problems
Shengxiang Yang, Jürgen Branke · 2005
Evolutionary algorithms (EAs) have been widely applied to solve stationary optimization problems. However, many real-world optimization problems are actually dynamic. For example, new jobs are to be added to the schedule, the quality of the raw material may be changing, and new orders have to be included into the vehicle routing problem etc. In such cases, when the problem changes over the course of the optimization, the purpose of the optimization algorithm changes from finding an optimal solution to being able to continuously track the movement of the optimum over time. This seriously challenges traditional EAs since they cannot adapt well to the changing environment once converged.