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	Algorithms for multi-task optimization span a wide array of real-world applications. Recent studies highlight the potential for speed-ups in the optimization of engineering design parameters by conducting related designs jointly in a multi-task manner.<ref name=cognitive/> In [[machine learning]], the transfer of optimized features across related data sets can enhance the efficiency of the training process as well as improve the generalization capability of learned models.<ref>Chandra, R., Gupta, A., Ong, Y. S., & Goh, C. K. (2016, October). [http://www.cil.ntu.edu.sg/mfo/downloads/cvmultask.pdf Evolutionary multi-task learning for modular training of feedforward neural networks]. In International Conference on Neural Information Processing (pp. 37-46). Springer, Cham.</ref><ref>Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). [http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-n%E2%80%A6 How transferable are features in deep neural networks?] In Advances in neural information processing systems (pp. 3320-3328).</ref> In addition, the concept of multi-tasking has led to advances in automatic [[hyperparameter optimization]] of machine learning models and [[ensemble learning]].<ref>{{cite book \| doi=10.1109/CEC.2016.7748363 \| chapter=Learning ensemble of decision trees through multifactorial genetic programming \| title=2016 IEEE Congress on Evolutionary Computation (CEC) \| year=2016 \| last1=Wen \| first1=Yu-Wei \| last2=Ting \| first2=Chuan-Kang \| pages=5293–5300 \| isbn=978-1-5090-0623-6 \| s2cid=2617811 }}</ref><ref>{{cite book \| doi=10.1145/3205455.3205638 \| chapter=Evolutionary feature subspaces generation for ensemble classification \| title=Proceedings of the Genetic and Evolutionary Computation Conference \| year=2018 \| last1=Zhang \| first1=Boyu \| last2=Qin \| first2=A. K. \| last3=Sellis \| first3=Timos \| pages=577–584 \| isbn=978-1-4503-5618-3 \| s2cid=49564862 }}</ref>		Algorithms for multi-task optimization span a wide array of real-world applications. Recent studies highlight the potential for speed-ups in the optimization of engineering design parameters by conducting related designs jointly in a multi-task manner.<ref name=cognitive/> In [[machine learning]], the transfer of optimized features across related data sets can enhance the efficiency of the training process as well as improve the generalization capability of learned models.<ref>Chandra, R., Gupta, A., Ong, Y. S., & Goh, C. K. (2016, October). [http://www.cil.ntu.edu.sg/mfo/downloads/cvmultask.pdf Evolutionary multi-task learning for modular training of feedforward neural networks]. In International Conference on Neural Information Processing (pp. 37-46). Springer, Cham.</ref><ref>Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). [http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-n%E2%80%A6 How transferable are features in deep neural networks?] In Advances in neural information processing systems (pp. 3320-3328).</ref> In addition, the concept of multi-tasking has led to advances in automatic [[hyperparameter optimization]] of machine learning models and [[ensemble learning]].<ref>{{cite book \| doi=10.1109/CEC.2016.7748363 \| chapter=Learning ensemble of decision trees through multifactorial genetic programming \| title=2016 IEEE Congress on Evolutionary Computation (CEC) \| year=2016 \| last1=Wen \| first1=Yu-Wei \| last2=Ting \| first2=Chuan-Kang \| pages=5293–5300 \| isbn=978-1-5090-0623-6 \| s2cid=2617811 }}</ref><ref>{{cite book \| doi=10.1145/3205455.3205638 \| chapter=Evolutionary feature subspaces generation for ensemble classification \| title=Proceedings of the Genetic and Evolutionary Computation Conference \| year=2018 \| last1=Zhang \| first1=Boyu \| last2=Qin \| first2=A. K. \| last3=Sellis \| first3=Timos \| pages=577–584 \| isbn=978-1-4503-5618-3 \| s2cid=49564862 }}</ref>

	Applications have also been reported in cloud computing,<ref>{{cite book \| doi=10.1007/978-3-319-94472-2_10 \| chapter=An Evolutionary Multitasking Algorithm for Cloud Computing Service Composition \| title=Services – SERVICES 2018 \| series=Lecture Notes in Computer Science \| year=2018 \| last1=Bao \| first1=Liang \| last2=Qi \| first2=Yutao \| last3=Shen \| first3=Mengqing \| last4=Bu \| first4=Xiaoxuan \| last5=Yu \| first5=Jusheng \| last6=Li \| first6=Qian \| last7=Chen \| first7=Ping \| volume=10975 \| pages=130–144 \| isbn=978-3-319-94471-5 }}</ref> with future developments geared towards cloud-based on-demand optimization services that can cater to multiple customers simultaneously.<ref name=mfo/><ref>Tang, J., Chen, Y., Deng, Z., Xiang, Y., & Joy, C. P. (2018). [https://www.ijcai.org/proceedings/2018/0538.pdf A Group-based Approach to Improve Multifactorial Evolutionary Algorithm]. In IJCAI (pp. 3870-3876).</ref> Recent work has additionally shown applications in chemistry.<ref>{{citation \|mode=cs1 \|doi=10.26434/chemrxiv.13250216.v2 \|title=Multi-task Bayesian Optimization of Chemical Reactions \|work=chemRxiv \|date=2021 \|last1=Felton \|first1=Kobi \|last2=Wigh \|first2=Daniel \|last3=Lapkin \|first3=Alexei}}</ref> In addition, some recent works have applied multi-task optimization algorithms in industrial manufacturing.<ref>{{Cite journal \|last1=Jiang \|first1=Yi \|last2=Zhan \|first2=Zhi-Hui \|last3=Tan \|first3=Kay Chen \|last4=Zhang \|first4=Jun \|date=October 2023 \|title=A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization \|journal=IEEE Transactions on Evolutionary Computation \|volume=27 \|issue=5 \|pages=1514–1528 \|doi=10.1109/TEVC.2022.3210783 \|issn=1089-778X\|doi-access=free }}</ref><ref>{{Cite journal \|last1=Jiang \|first1=Yi \|last2=Zhan \|first2=Zhi-Hui \|last3=Tan \|first3=Kay Chen \|last4=Kwong \|first4=Sam \|last5=Zhang \|first5=Jun \|date=2024 \|title=Knowledge Structure Preserving-Based Evolutionary Many-Task Optimization \|journal=IEEE Transactions on Evolutionary Computation \|volume=29 \|issue=2 \|pages=287–301 \|doi=10.1109/TEVC.2024.3355781 \|issn=1089-778X\|doi-access=free }}</ref>		Applications have also been reported in cloud computing,<ref>{{cite book \| doi=10.1007/978-3-319-94472-2_10 \| chapter=An Evolutionary Multitasking Algorithm for Cloud Computing Service Composition \| title=Services – SERVICES 2018 \| series=Lecture Notes in Computer Science \| year=2018 \| last1=Bao \| first1=Liang \| last2=Qi \| first2=Yutao \| last3=Shen \| first3=Mengqing \| last4=Bu \| first4=Xiaoxuan \| last5=Yu \| first5=Jusheng \| last6=Li \| first6=Qian \| last7=Chen \| first7=Ping \| volume=10975 \| pages=130–144 \| isbn=978-3-319-94471-5 }}</ref> with future developments geared towards cloud-based on-demand optimization services that can cater to multiple customers simultaneously.<ref name=mfo/><ref>Tang, J., Chen, Y., Deng, Z., Xiang, Y., & Joy, C. P. (2018). [https://www.ijcai.org/proceedings/2018/0538.pdf A Group-based Approach to Improve Multifactorial Evolutionary Algorithm]. In IJCAI (pp. 3870-3876).</ref> Recent work has additionally shown applications in chemistry.<ref>{{citation \|mode=cs1 \|doi=10.26434/chemrxiv.13250216.v2 \|title=Multi-task Bayesian Optimization of Chemical Reactions \|work=chemRxiv \|date=2021 \|last1=Felton \|first1=Kobi \|last2=Wigh \|first2=Daniel \|last3=Lapkin \|first3=Alexei\|doi-access=free }}</ref> In addition, some recent works have applied multi-task optimization algorithms in industrial manufacturing.<ref>{{Cite journal \|last1=Jiang \|first1=Yi \|last2=Zhan \|first2=Zhi-Hui \|last3=Tan \|first3=Kay Chen \|last4=Zhang \|first4=Jun \|date=October 2023 \|title=A Bi-Objective Knowledge Transfer Framework for Evolutionary Many-Task Optimization \|journal=IEEE Transactions on Evolutionary Computation \|volume=27 \|issue=5 \|pages=1514–1528 \|doi=10.1109/TEVC.2022.3210783 \|issn=1089-778X\|doi-access=free }}</ref><ref>{{Cite journal \|last1=Jiang \|first1=Yi \|last2=Zhan \|first2=Zhi-Hui \|last3=Tan \|first3=Kay Chen \|last4=Kwong \|first4=Sam \|last5=Zhang \|first5=Jun \|date=2024 \|title=Knowledge Structure Preserving-Based Evolutionary Many-Task Optimization \|journal=IEEE Transactions on Evolutionary Computation \|volume=29 \|issue=2 \|pages=287–301 \|doi=10.1109/TEVC.2024.3355781 \|issn=1089-778X\|doi-access=free }}</ref>

	== Mathematics ==		== Mathematics ==

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2025-05-22T19:30:12Z

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Multi-task learning - Revision history

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