{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:20:51Z","timestamp":1764937251512,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T00:00:00Z","timestamp":1614211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Genetic algorithms (GA\u2019s) are mostly used as an offline optimisation method to discover a suitable solution to a complex problem prior to implementation. In this paper, we present a different application in which a GA is used to progressively adapt the collective performance of an ad hoc collection of devices that are being integrated post-deployment. Adaptive behaviour in the context of this article refers to two dynamic aspects of the problem: (a) the availability of individual devices as well as the objective functions for the performance of the entire population. We illustrate this concept in a video surveillance scenario in which already installed cameras are being retrofitted with networking capabilities to form a coherent closed-circuit television (CCTV) system. We show that this can be conceived as a multi-objective optimisation problem which can be solved at run-time, with the added benefit that solutions can be refined or modified in response to changing priorities or even unpredictable events such as faults. We present results of a detailed simulation study, the implications of which are being discussed from both a theoretical and practical viewpoint (trade-off between saving computational resources and surveillance coverage).<\/jats:p>","DOI":"10.3390\/a14030074","type":"journal-article","created":{"date-parts":[[2021,2,25]],"date-time":"2021-02-25T21:16:53Z","timestamp":1614287813000},"page":"74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Behaviour for a Self-Organising Video Surveillance System Using a Genetic Algorithm"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1194-5408","authenticated-orcid":false,"given":"Fabrice","family":"Saffre","sequence":"first","affiliation":[{"name":"VTT\u2014Technical Research Centre of Finland Ltd., MIKES bldg, Tekniikantie 1, 02150 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1471-5774","authenticated-orcid":false,"given":"Hanno","family":"Hildmann","sequence":"additional","affiliation":[{"name":"TNO\u2014Netherlands Organisation for Applied Scientific Research, Oude Waalsdorperweg 63, 2597 AK Den Haag, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Medina, B.E., and Manera, L.T. 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