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Intell."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The quantity and positioning of glazing on a building\u2019s facade has a strong influence on the building\u2019s heating, lighting, and cooling performance. Evolutionary algorithms have been effective in finding glazing layouts that optimise the trade-offs between these properties. However, this is time-consuming, needing many calls to a building performance simulation. Surrogate fitness functions have been used previously to speed up optimisation without compromising solution quality; our novelty is in the application of a surrogate to a binary encoded, multi-objective, building optimisation problem. We propose and demonstrate a process to choose a suitable model type for the surrogate: a multilayer perceptron (MLP) is found to work best in this case. The MLP is integrated with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, and experimental results show that the surrogate leads to a significant (400x) speedup. This allows the algorithm to find solutions that are better than the algorithm without a surrogate in the same timeframe. Updating the surrogate at intervals improves the solution quality further with a modest increase in run time.<\/jats:p>","DOI":"10.1007\/s44244-025-00025-1","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:36:34Z","timestamp":1747215394000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Surrogate-assisted evolutionary multi-objective optimisation of office building glazing"],"prefix":"10.1007","volume":"3","author":[{"given":"Alexander E. 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