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Dang, Duc-Cuong, Jansen, Thomas and Lehre, Per Kristian (2016) Populations can be essential in tracking dynamic optima. Algorithmica . ISSN 1432-0541

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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.

Item Type:	Article
RIS ID:	https://nottingham-repository.worktribe.com/output/804331
Keywords:	Runtime Analysis, Population-based Algorithm, Dynamic Optimisation
Schools/Departments:	University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:	10.1007/s00453-016-0187-y
Depositing User:	Dang, Duc-cuong
Date Deposited:	14 Jul 2016 09:01
Last Modified:	04 May 2020 18:05
URI:	https://eprints.nottingham.ac.uk/id/eprint/34913

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