Populations can be essential in tracking dynamic optima

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

Abstract

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.

Citation

Dang, D.-C., Jansen, T., & Lehre, P. K. (in press). Populations can be essential in tracking dynamic optima. Algorithmica, https://doi.org/10.1007/s00453-016-0187-y

• 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
• DOI: https://doi.org/10.1007/s00453-016-0187-y
• Keywords: Runtime Analysis, Population-based Algorithm, Dynamic Optimisation
• Public URL: https://nottingham-repository.worktribe.com/output/804331
• Publisher URL: http://link.springer.com/article/10.1007/s00453-016-0187-y
• Contract Date: Jul 14, 2016