{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T11:32:45Z","timestamp":1793014365027,"version":"4.5.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T00:00:00Z","timestamp":1671667200000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-022-01537-8","type":"journal-article","created":{"date-parts":[[2022,12,22]],"date-time":"2022-12-22T13:12:43Z","timestamp":1671714763000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Genetic Algorithm Based Hyper-Parameter Tuning to Improve the Performance of Machine Learning Models"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8312-7821","authenticated-orcid":false,"given":"D. L.","family":"Shanthi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Chethan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"1537_CR1","doi-asserted-by":"publisher","first-page":"8091","DOI":"10.1007\/s11042-020-10139-6","volume":"80","author":"S Katoch","year":"2021","unstructured":"Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl. 2021;80:8091\u2013126. https:\/\/doi.org\/10.1007\/s11042-020-10139-6.","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"1537_CR2","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160. https:\/\/doi.org\/10.1007\/s42979-021-00592-x. (Epub 2021 Mar 22. PMID: 33778771; PMCID: PMC7983091).","journal-title":"SN Comput Sci."},{"key":"1537_CR3","doi-asserted-by":"crossref","unstructured":"Hammad I, El-Sankary K, Gu J. A comparative study on machine learning algorithms for the control of a wall following robot. In: 2019 IEEE international conference on robotics and biomimetics (ROBIO). IEEE; 2019.","DOI":"10.1109\/ROBIO49542.2019.8961836"},{"key":"1537_CR4","doi-asserted-by":"crossref","unstructured":"Kumar Y, Kaur K, Singh G. Machine learning aspects and its applications towards different research areas. In: 2020 international conference on computation, automation and knowledge management (ICCAKM); 2020.","DOI":"10.1109\/ICCAKM46823.2020.9051502"},{"key":"1537_CR5","doi-asserted-by":"crossref","unstructured":"Olawade OE, Onashoga SA, Arogundade O. Comparative analysis of machine learning techniques in health system. In: 2020 international conference in mathematics, computer engineering and computer science (ICMCECS); 2020.","DOI":"10.1109\/ICMCECS47690.2020.240861"},{"key":"1537_CR6","doi-asserted-by":"crossref","unstructured":"Wang Z, Agung M, Egawa R, Suda R, Takizawa H. Automatic hyperparameter tuning of machine learning models under time constraints. In: 2018 IEEE international conference on big data (Big Data); 2018.","DOI":"10.1109\/BigData.2018.8622384"},{"issue":"4\/5","key":"1537_CR7","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1147\/JRD.2017.2709578","volume":"61","author":"GI Diaz","year":"2017","unstructured":"Diaz GI, Fokoue-Nkoutche A, Nannicini G, Samulowitz H. An effective algorithm for hyperparameter optimization of neural networks. IBM J Res Dev. 2017;61(4\/5):91\u2013911.","journal-title":"IBM J Res Dev"},{"key":"1537_CR8","doi-asserted-by":"crossref","unstructured":"Shekar BH, Dagnew G. Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data. In: 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP); 2019","DOI":"10.1109\/ICACCP.2019.8882943"},{"issue":"8","key":"1537_CR9","doi-asserted-by":"publisher","first-page":"3668","DOI":"10.1109\/TCYB.2019.2950779","volume":"50","author":"S Sun","year":"2020","unstructured":"Sun S, Cao Z, Zhu H, Zhao J. A survey of optimization methods from a machine learning perspective. IEEE Trans Cybern. 2020;50(8):3668\u201381. https:\/\/doi.org\/10.1109\/TCYB.2019.2950779. (Epub 2019 Nov 18 PMID: 31751262).","journal-title":"IEEE Trans Cybern"},{"key":"1537_CR10","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT Press; 2016."},{"key":"1537_CR11","unstructured":"https:\/\/towardsdatascience.com\/tpot-automated-machine-learning-in-pythone56800e69c11"},{"key":"1537_CR12","unstructured":"https:\/\/www.kdnuggets.com\/2016\/05\/tpot-python-automating-data-science.html\/2"},{"key":"1537_CR13","doi-asserted-by":"publisher","unstructured":"Al-Oqaily AT, Shakah G. Solving non-linear optimization problems using parallel genetic algorithm. In: 2018 8th international conference on computer science and information technology (CSIT); 2018, pp 103\u2013106, https:\/\/doi.org\/10.1109\/CSIT.2018.8486176","DOI":"10.1109\/CSIT.2018.8486176"},{"key":"1537_CR14","unstructured":"https:\/\/medium.com\/cindicator\/genetic-algorithms-and-hyperparameters-weekend-of-a-data-scientist-8f069669015e"},{"key":"1537_CR15","doi-asserted-by":"publisher","unstructured":"Mostaeen G, Svajlenko J, Roy B, Roy CK, Schneider KA. [Research Paper] On the use of machine learning techniques towards the design of cloud based automatic code clone validation tools. In: 2018 IEEE 18th international working conference on source code analysis and manipulation (SCAM), 2018, pp. 155\u2013164, https:\/\/doi.org\/10.1109\/SCAM.2018.00025.","DOI":"10.1109\/SCAM.2018.00025"},{"key":"1537_CR16","unstructured":"Automated Hyperparameter Tuning in: Hyperparameter Optimization: https:\/\/towardsdatascience.com\/hyperparameters-optimization-526348bb8e2d"},{"issue":"1","key":"1537_CR17","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","volume":"40","author":"C Girish","year":"2014","unstructured":"Girish C, Ferat S. A survey on feature selection methods. Comput Electr Eng. 2014;40(1):16\u201328. https:\/\/doi.org\/10.1016\/j.compeleceng.2013.11.024. (ISSN 0045\u20137906).","journal-title":"Comput Electr Eng"},{"issue":"90","key":"1537_CR18","doi-asserted-by":"publisher","first-page":"3133","DOI":"10.5555\/2627435.2697065","volume":"15","author":"F-D Manuel","year":"2014","unstructured":"Manuel F-D, Eva C, Senen B, Dinani A. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15(90):3133\u201381. https:\/\/doi.org\/10.5555\/2627435.2697065.","journal-title":"J Mach Learn Res"},{"key":"1537_CR19","unstructured":"Online content: Hyperparameter Optimization With Random Search and Grid Search - MachineLearningMastery.com accessed on 14 October 2022."},{"key":"1537_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6849-3","volume-title":"Applied predictive modeling","author":"K Max","year":"2013","unstructured":"Philipp P, Anne-Laure B, Bernd B. Tunability: importance of hyperparameters of machine learning algorithms. J Mach Learn. 2021;20(1):1934\u201365 https:\/\/doi.org\/10.1007\/978-1-4614-6849-3 (ISSN:1532\u20134435)."},{"issue":"1","key":"1537_CR21","doi-asserted-by":"publisher","first-page":"26","DOI":"10.11989\/JEST.1674-862X.80904120","volume":"17","author":"W Jia","year":"2019","unstructured":"Jia W, Xiu-Yun C, Hao Z, Li-Dong X, Hang L, Si-Hao D. Hyperparameter optimization for machine learning models based on bayesian optimization. J Electron Sci Technol. 2019;17(1):26\u201340. https:\/\/doi.org\/10.11989\/JEST.1674-862X.80904120. (ISSN 1674\u2013862X).","journal-title":"J Electron Sci Technol"},{"issue":"1","key":"1537_CR22","first-page":"1934","volume":"20","author":"P Philipp","year":"2021","unstructured":"Philipp P, Anne-Laure B, Bernd B. Tunability: importance of hyperparameters of machine learning algorithms. J Mach Learn. 2021;20(1):1934\u201365 (ISSN:1532\u20134435).","journal-title":"J Mach Learn"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01537-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-022-01537-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01537-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T11:43:10Z","timestamp":1678448590000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-022-01537-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,22]]},"references-count":22,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["1537"],"URL":"https:\/\/doi.org\/10.1007\/s42979-022-01537-8","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,22]]},"assertion":[{"value":"27 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"119"}}