AUTO: supervised learning with full model search and global optimisation
The AUTO algorithm is presented to incrementally build models and solve supervised learning problems. AUTO uses traditional derivative-free optimisation, like genetic algorithms, to search the problem space of arbitrary functions. With reinforcement learning, AUTO learns actions to guide its search...
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| Published in | Journal of experimental & theoretical artificial intelligence Vol. 36; no. 8; pp. 1619 - 1630 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Abingdon
Taylor & Francis
16.11.2024
Taylor & Francis Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0952-813X 1362-3079 |
| DOI | 10.1080/0952813X.2023.2165717 |
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| Abstract | The AUTO algorithm is presented to incrementally build models and solve supervised learning problems. AUTO uses traditional derivative-free optimisation, like genetic algorithms, to search the problem space of arbitrary functions. With reinforcement learning, AUTO learns actions to guide its search process and gradually improve performance. A comparative analysis is presented exploring supervised learning performance and interpretability of AUTO models. Results indicate AUTO outperforms genetic programming and rivals multilayer perceptrons. AUTO automatically builds models to closely match datasets without the limited search space of traditional supervised learning. With enough time and computational resources, AUTO can generate any function. |
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| AbstractList | The AUTO algorithm is presented to incrementally build models and solve supervised learning problems. AUTO uses traditional derivative-free optimisation, like genetic algorithms, to search the problem space of arbitrary functions. With reinforcement learning, AUTO learns actions to guide its search process and gradually improve performance. A comparative analysis is presented exploring supervised learning performance and interpretability of AUTO models. Results indicate AUTO outperforms genetic programming and rivals multilayer perceptrons. AUTO automatically builds models to closely match datasets without the limited search space of traditional supervised learning. With enough time and computational resources, AUTO can generate any function. |
| Author | Valova, Iren Lovinger, Justin |
| Author_xml | – sequence: 1 givenname: Justin surname: Lovinger fullname: Lovinger, Justin email: auto@justinlovinger.com organization: University of Massachusetts Dartmouth – sequence: 2 givenname: Iren surname: Valova fullname: Valova, Iren organization: University of Massachusetts Dartmouth |
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| Cites_doi | 10.1016/j.swevo.2017.09.008 10.1109/TSMCC.2009.2033566 10.1109/5254.671091 10.1016/j.chb.2014.09.034 10.1109/TNN.2006.875977 10.1007/BF00175354 10.1090/S0025-5718-1970-0274029-X 10.2307/1909768 10.1109/34.291440 10.1007/s00500-018-03729-y 10.1007/BF01589116 10.1090/S0025-5718-1970-0258249-6 10.1093/imamat/6.3.222 10.1016/S0167-7152(96)00140-X 10.1016/j.cam.2018.09.012 10.1016/j.procs.2014.09.033 10.1016/j.knosys.2011.04.014 10.1109/TNN.2004.836241 10.1109/TCYB.2020.3032945 10.1007/978-0-387-30164-8_630 10.1093/imamat/6.1.76 10.1016/j.ymssp.2004.03.002 10.1016/j.ijepes.2021.106988 10.1137/S1052623401383455 10.1109/4235.996017 10.1016/j.esr.2021.100760 10.1093/comjnl/13.3.317 10.1016/j.jsv.2005.09.004 10.1016/j.energy.2020.118306 10.1016/j.amc.2019.124777 10.1109/ICASSP.2013.6639016 |
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| SubjectTerms | Genetic algorithms genetic programming Global optimization Machine learning machine programming Mathematical optimization Multilayer perceptrons Performance enhancement Search process Supervised learning |
| Title | AUTO: supervised learning with full model search and global optimisation |
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