Abstract — Many random events usually are associated with executions of operational plans at various companies and organizations. For example, some tasks might be delayed and/or executed earlier. Some operational constraints can be introduced due to new regulations or business rules. In some cases, there might be a shift in the relative importance of objectives associated with these plans. All these potential modifications create a huge pressure on planning staff for generating plans that can adapt quickly to changes in environment during execution. In this paper we address adaptation in dynamic environments. Many researchers in evolutionary community addressed the problem of optimization in dynamic environments. Through an overview on applying evolutionary algorithms for solving dynamic optimization problems, we classify the work into two main categories: (1) finding/tracking optima and (2) adaptatio
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