Abstract
As optimization problems continue to become more complex, previous studies have demonstrated that algorithm performance varies depending on the specific problem being addressed. Thus, this study proposes an adaptive data-driven recommendation model based on the stacked autoencoder. This approach involves the use of a meta-learning autoencoder that is stacked with multiple supervised autoencoders, generating deep meta-features. Then the proper algorithms are identified to address the new problems. To verify the feasibility of this proposed model, experiments are conducted using benchmark functions. Experimental results indicate that both instance-based and model-based meta-learners are well suited to the advanced model, and the performance is promising.
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Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (No. 71971142 and 71501132), the Natural Science Foundation of Guangdong Province (No. 2022A1515010278 and 2021A1515110595).
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Ma, Y., Pang, Y., Li, S., Qu, Y., Wang, Y., Chu, X. (2023). A Stacked Autoencoder Based Meta-Learning Model for Global Optimization. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_17
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DOI: https://doi.org/10.1007/978-981-99-5844-3_17
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