Populations can be essential in tracking dynamic optima
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...
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| Format: | Article |
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Springer
2016
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| Online Access: | https://eprints.nottingham.ac.uk/34913/ |
| _version_ | 1848794961276829696 |
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| author | Dang, Duc-Cuong Jansen, Thomas Lehre, Per Kristian |
| author_facet | Dang, Duc-Cuong Jansen, Thomas Lehre, Per Kristian |
| author_sort | Dang, Duc-Cuong |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:24:30Z |
| format | Article |
| id | nottingham-34913 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:24:30Z |
| publishDate | 2016 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-349132020-05-04T18:05:38Z https://eprints.nottingham.ac.uk/34913/ Populations can be essential in tracking dynamic optima Dang, Duc-Cuong Jansen, Thomas Lehre, Per Kristian 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. Springer 2016-08-26 Article PeerReviewed Dang, Duc-Cuong, Jansen, Thomas and Lehre, Per Kristian (2016) Populations can be essential in tracking dynamic optima. Algorithmica . ISSN 1432-0541 Runtime Analysis Population-based Algorithm Dynamic Optimisation http://link.springer.com/article/10.1007/s00453-016-0187-y doi:10.1007/s00453-016-0187-y doi:10.1007/s00453-016-0187-y |
| spellingShingle | Runtime Analysis Population-based Algorithm Dynamic Optimisation Dang, Duc-Cuong Jansen, Thomas Lehre, Per Kristian Populations can be essential in tracking dynamic optima |
| title | Populations can be essential in tracking dynamic optima |
| title_full | Populations can be essential in tracking dynamic optima |
| title_fullStr | Populations can be essential in tracking dynamic optima |
| title_full_unstemmed | Populations can be essential in tracking dynamic optima |
| title_short | Populations can be essential in tracking dynamic optima |
| title_sort | populations can be essential in tracking dynamic optima |
| topic | Runtime Analysis Population-based Algorithm Dynamic Optimisation |
| url | https://eprints.nottingham.ac.uk/34913/ https://eprints.nottingham.ac.uk/34913/ https://eprints.nottingham.ac.uk/34913/ |