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Real-world optimisation problems are often dynamic. Previously good solutionsmust be updated or replaced due to changes in objectives and constraints. It isoften claimed that evolutionary algorithms are particularly suitable fordynamic optimisation because a large population can contain different solutionsthat may be useful in the future. However, rigorous theoretical demonstrationsfor how populations in dynamic optimisation can be essential are sparse andrestricted to special cases.  This paper provides theoretical explanations of how populations can beessential in evolutionary dynamic optimisation in a general and naturalsetting. We describe a natural class of dynamic optimisation problems where asufficiently large population is necessary to keep track of moving optimareliably. We establish a relationship between the population-size and theprobability that the algorithm loses track of the optimum.

Populations can be essential in tracking dynamic optima