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Populations can be essential in tracking dynamic optima

Dang, Duc-Cuong; Jansen, Thomas; Lehre, Per Kristian


Duc-Cuong Dang

Thomas Jansen

Per Kristian Lehre


Real-world optimisation problems are often dynamic. Previously good solutions must be updated or replaced due to changes in objectives and constraints. It is often claimed that evolutionary algorithms are particularly suitable for dynamic optimisation because a large population can contain different solutions that may be useful in the future. However, rigorous theoretical demonstrations for how populations in dynamic optimisation can be essential are sparse and restricted to special cases.

This paper provides theoretical explanations of how populations can be essential in evolutionary dynamic optimisation in a general and natural setting. We describe a natural class of dynamic optimisation problems where a sufficiently large population is necessary to keep track of moving optima reliably. We establish a relationship between the population-size and the probability that the algorithm loses track of the optimum.


Dang, D., Jansen, T., & Lehre, P. K. (in press). Populations can be essential in tracking dynamic optima. Algorithmica,

Journal Article Type Article
Acceptance Date Jul 8, 2016
Online Publication Date Aug 26, 2016
Deposit Date Jul 14, 2016
Publicly Available Date Aug 26, 2016
Journal Algorithmica
Print ISSN 0178-4617
Electronic ISSN 1432-0541
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Keywords Runtime Analysis, Population-based Algorithm, Dynamic Optimisation
Public URL
Publisher URL


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