{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T08:25:29Z","timestamp":1771489529603,"version":"3.50.1"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032179326","type":"print"},{"value":"9783032179333","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-17933-3_16","type":"book-chapter","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:36:48Z","timestamp":1771486608000},"page":"152-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Impact of\u00a0Crossover and\u00a0Mutation on\u00a0Carbon Emissions in\u00a0Real-Coded Genetic Algorithm: An Empirical Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1831-6148","authenticated-orcid":false,"given":"Nancy","family":"P\u00e9rez-Castro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1565-5267","authenticated-orcid":false,"given":"Efr\u00e9n","family":"Mezura-Montes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0935-7642","authenticated-orcid":false,"given":"H\u00e9ctor-Gabriel","family":"Acosta-Mesa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"issue":"12","key":"16_CR1","doi-asserted-by":"publisher","first-page":"3516","DOI":"10.4236\/ojapps.2024.1412230","volume":"14","author":"OR Ajao","year":"2024","unstructured":"Ajao, O.R.: Optimizing energy infrastructure with AI technology: a literature review. Open J. Appl. Sci. 14(12), 3516\u20133544 (2024). https:\/\/doi.org\/10.4236\/ojapps.2024.1412230","journal-title":"Open J. Appl. Sci."},{"issue":"1","key":"16_CR2","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/s44163-024-00211-7","volume":"4","author":"P Biswas","year":"2024","unstructured":"Biswas, P., Rashid, A., Biswas, A., Nasim, M.A.A., Chakraborty, S., Gupta, K.D., George, R.: AI-driven approaches for optimizing power consumption: a comprehensive survey. Discover Artif. Intell. 4(1), 116 (2024). https:\/\/doi.org\/10.1007\/s44163-024-00211-7","journal-title":"Discover Artif. Intell."},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Courty, B., et al.: MinervaBooks: mlco2\/codecarbon: v2.4.1 (May 2024). https:\/\/doi.org\/10.5281\/zenodo.11171501, https:\/\/doi.org\/10.5281\/zenodo.11171501","DOI":"10.5281\/zenodo.11171501"},{"key":"16_CR4","unstructured":"Fortin, F.A., Rainville, F.M.D., Gardner, M.A., Parizeau, M., Gagn\u00e9, C.: Deap: evolutionary algorithms made easy. J. Mach. Learn. Res. 13(70), 2171\u20132175 (2012). http:\/\/jmlr.org\/papers\/v13\/fortin12a.html"},{"key":"16_CR5","doi-asserted-by":"publisher","unstructured":"Gen, M., Lin, L.: Genetic algorithms and their applications. In: Pham, H. (ed.) Springer Handbook of Engineering Statistics, pp. 635\u2013674. Springer, London (2023). https:\/\/doi.org\/10.1007\/978-1-4471-7503-2_33, series Title: Springer Handbooks","DOI":"10.1007\/978-1-4471-7503-2_33"},{"key":"16_CR6","volume-title":"Adaptation in Natural and Artificial Systems","author":"JH Holland","year":"1975","unstructured":"Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)"},{"key":"16_CR7","doi-asserted-by":"publisher","unstructured":"Jegham, N., Abdelatti, M., Elmoubarki, L., Hendawi, A.: How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference (2025). https:\/\/doi.org\/10.48550\/ARXIV.2505.09598, version Number: 3","DOI":"10.48550\/ARXIV.2505.09598"},{"key":"16_CR8","doi-asserted-by":"publisher","unstructured":"Morrison, J., Na, C., Fernandez, J., Dettmers, T., Strubell, E., Dodge, J.: Holistically Evaluating the Environmental Impact of Creating Language Models (2025). https:\/\/doi.org\/10.48550\/ARXIV.2503.05804, version Number: 1","DOI":"10.48550\/ARXIV.2503.05804"},{"issue":"11","key":"16_CR9","doi-asserted-by":"publisher","first-page":"2810","DOI":"10.3390\/en18112810","volume":"18","author":"R R\u00f3\u017cycki","year":"2025","unstructured":"R\u00f3\u017cycki, R., Solarska, D.A., Walig\u00f3ra, G.: Energy-aware machine learning models\u2013a review of recent techniques and perspectives. Energies 18(11), 2810 (2025). https:\/\/doi.org\/10.3390\/en18112810","journal-title":"Energies"},{"key":"16_CR10","doi-asserted-by":"publisher","unstructured":"Schneider, I., Xu, H., Benecke, S., Patterson, D., Huang, K., Ranganathan, P., Elsworth, C.: Life-cycle emissions of AI hardware: a cradle-to-grave approach and generational trends (2025). https:\/\/doi.org\/10.48550\/ARXIV.2502.01671, version Number: 1","DOI":"10.48550\/ARXIV.2502.01671"},{"key":"16_CR11","doi-asserted-by":"publisher","unstructured":"Vente, T., Wegmeth, L., Said, A., Beel, J.: From clicks to carbon: The environmental toll of recommender systems, pp. 580\u2013590. RecSys \u201924, Association for Computing Machinery, New York, NY, USA (2024). https:\/\/doi.org\/10.1145\/3640457.3688074","DOI":"10.1145\/3640457.3688074"},{"key":"16_CR12","doi-asserted-by":"publisher","unstructured":"Verdecchia, R., Cruz, L., Sallou, J., Lin, M., Wickenden, J., Hotellier, E.: Data-centric green AI an exploratory empirical study. In: 2022 International Conference on ICT for Sustainability (ICT4S). pp. 35\u201345. IEEE, Plovdiv, Bulgaria, June 2022. https:\/\/doi.org\/10.1109\/ICT4S55073.2022.00015","DOI":"10.1109\/ICT4S55073.2022.00015"},{"key":"16_CR13","doi-asserted-by":"publisher","unstructured":"Xu, Y., Mart\u00ednez-Fern\u00e1ndez, S., Martinez, M., Franch, X.: Energy efficiency of training neural network architectures: an empirical study (2023). https:\/\/doi.org\/10.48550\/ARXIV.2302.00967, publisher: arXiv Version Number: 1","DOI":"10.48550\/ARXIV.2302.00967"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence. MICAI 2025 International Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-17933-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:36:50Z","timestamp":1771486610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-17933-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032179326","9783032179333"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-17933-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"20 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guanajuato","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/micai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}