Scaling Multiobjective Evolution to Large Data With Minions: A Bayes-Informed Multitask Approach

In an era of pervasive digitalization, the growing volume and variety of data streams poses a new challenge to the efficient running of data-driven optimization algorithms. Targeting scalable multiobjective evolution under large-instance data, this article proposes the general idea of using subsampl...

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Vydáno v:IEEE transactions on cybernetics Ročník 54; číslo 2; s. 1 - 14
Hlavní autoři: Chen, Zefeng, Gupta, Abhishek, Zhou, Lei, Ong, Yew-Soon
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Approximation
Artificial neural networks
Bayes resource allocation
Computer science
Data transmission
data-driven multiobjective optimization
Datasets
Digitization
Evolution
evolutionary multitasking (EMT)
Linear programming
Multiple objective analysis
Multitasking
Optimization
Parallel processing
Resource allocation
Resource management
Task analysis
Uncertainty
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:In an era of pervasive digitalization, the growing volume and variety of data streams poses a new challenge to the efficient running of data-driven optimization algorithms. Targeting scalable multiobjective evolution under large-instance data, this article proposes the general idea of using subsampled small-data tasks as helpful minions (i.e., auxiliary source tasks) to quickly optimize for large datasets-via an evolutionary multitasking framework. Within this framework, a novel computational resource allocation strategy is designed to enable the effective utilization of the minions while guarding against harmful negative transfers. To this end, an intertask empirical correlation measure is defined and approximated via Bayes' rule, which is then used to allocate resources online in proportion to the inferred degree of source-target correlation. In the experiments, the performance of the proposed algorithm is verified on: 1) sample average approximations of benchmark multiobjective optimization problems under uncertainty and 2) practical multiobjective hyperparameter tuning of deep neural network models. The results show that the proposed algorithm can obtain up to about 73% speedup relative to existing approaches, demonstrating its ability to efficiently tackle real-world multiobjective optimization involving evaluations on large datasets.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3214825