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Optimized scheduling of Distributed Energy Resources (DERs) within the Energy Hub concept can address these challenges by increasing the flexibility in the grid. However, this scheduling task can be categorized as an NP-hard optimization problem and requires the use of powerful heuristic algorithms to solve it. One such heuristic approach is an Evolutionary Algorithm (EA), however, EAs solution quality may be poor w.r.t. solution time when considering complex scheduling tasks of DERs. In our work, we improve the applied EA optimization by considering the predicted optimization quality. More specifically, we use Machine Learning (ML) algorithms trained on previous solutions to forecast the optimization quality. Based on these predictions, the computational effort of the EA is directed to particularly difficult areas of the search space. We direct the effort of the EA by dynamic interval length assignment during the phenotype mapping of the solutions proposed by the EA. We evaluate our approach by comparing multiple ML forecast algorithms and show that our approach leads to a significant increase of the evaluated degree of fulfillment by up to 4.4%.<\/jats:p>","DOI":"10.1007\/978-3-031-74741-0_14","type":"book-chapter","created":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T16:29:35Z","timestamp":1729268975000},"page":"205-223","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Phenotype Mapping in\u00a0Evolutionary Algorithms for\u00a0Energy Hub Scheduling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8074-1399","authenticated-orcid":false,"given":"Rafael","family":"Poppenborg","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9197-1739","authenticated-orcid":false,"given":"Kaleb","family":"Phipps","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9713-3590","authenticated-orcid":false,"given":"Maximilian","family":"Beichter","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9115-670X","authenticated-orcid":false,"given":"Kevin","family":"F\u00f6rderer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9100-5496","authenticated-orcid":false,"given":"Ralf","family":"Mikut","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3572-9083","authenticated-orcid":false,"given":"Veit","family":"Hagenmeyer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,19]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.rser.2018.09.046","volume":"100","author":"A Ahmed","year":"2019","unstructured":"Ahmed, A., Khalid, M.: A review on the selected applications of forecasting models in renewable power systems. 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