Abstract
To improve the efficiency of problem-solving for complex optimization problems, meta-learning was applied in algorithm selection to choose the most appropriate algorithm recently. However, the common meta-learners are feature-sensitive, where the selection and extraction of meta-features impact the quality of algorithm recommendations. In this study, we propose a data-driven recommendation model to implement the intelligent algorithm selection based on deep meta-features. A new kind of supervised stacked Autoencoder, named meta-learning Autoencoder, has been designed to process the deep meta-feature which is suitable both for instance-based and model-based meta-learners. To evaluate the performance of the proposed model, experiments have been conducted on some benchmark problems. Experimental results show that the recommendation accuracy of the model achieves nearly 100% in the seen problems and more than 80% in the unseen problems.
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References
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Cui, C., Hu, M., Weir, J.D., Wu, T.: A recommendation system for meta-modeling: a meta-learning based approach. Expert Syst. Appl. 46, 33–44 (2016)
Chu, X., Cai, F., Cui, C., Hu, M., Li, L., Qin, Q.: Adaptive recommendation model using meta-learning for population-based algorithms. Inf. Sci. 476, 192–210 (2019)
Sehta, N., Thakar, U.: A meta-learning approach for algorithm selection for capacitated vehicle routing problems. In: Cyber-Physical, IoT, and Autonomous Systems in Industry 4.0, pp. 255–268 (2021)
Khan, I., Zhang, X., Rehman, M., Ali, R.: A literature survey and empirical study of meta-learning for classifier selection. IEEE Access 8, 10262–10281 (2020)
Cui, C., Wu, T., Hu, M., Weir, J.D., Li, X.: Short-term building energy model recommendation system: a meta-learning approach. Appl. Energy 172, 251–263 (2016)
Chu, X., et al.: Meta-feature extraction for multi-objective optimization problems. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2021. CCIS, vol. 1449, pp. 432–445. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-5188-5_31
Pulatov, D., Kotthof, L.: Utilizing software features for algorithm selection. In COSEAL Workshop, co-located with the 15th ACM/SIGEVO Workshop on Foundations of Genetic Algorithms (2019)
Beel, J., Tyrell, B., Bergman, E., Collins, A., Nagoor, S.: Siamese meta-learning and algorithm selection with ‘Algorithm-Performance Personas’ [Proposal]. ar**v preprint ar**v:2006.12328 (2020)
Tyrrell, B., Bergman, E., Jones, G.J., Beel, J.: Algorithm-performance personas ‘for Siamese meta-learning and automated algorithm selection. In 7th ICML Workshop on Automated Machine Learning (2020)
LeCun, Y.: Connexionist learning models (1987)
Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)
Gehring, J., Miao, Y., Metze, F., Waibel, A.: Extracting deep bottleneck features using stacked auto-encoders. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3377–3381. IEEE (2013)
Chen, J., Wu, Z., Zhang, J.: Driver identification based on hidden feature extraction by using adaptive nonnegativity-constrained autoencoder. Appl. Soft Comput. 74, 1–9 (2019)
Xu, J., **ang, L., Hang, R., Wu, J.: Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 999–1002. IEEE (2014)
Wang, Y., Yang, H., Yuan, X., Shardt, Y.A., Yang, C., Gui, W.: Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder. J. Process Control 92, 79–89 (2020)
Goldberger, J., Hinton, G.E., Roweis, S., Salakhutdinov, R.R.: Neighbourhood components analysis. In: Advances in Neural Information Processing Systems, 17 (2004)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
McKay, M.D., Beckman, R.J., Conover, W.J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 23,9 (1979)
Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 178(15), 2985–2999 (2008)
Omidvar, M.N., Li, X., Mei, Y., Yao, X.: Cooperative co-evolution with differential grou** for large scale optimization. IEEE Trans. Evol. Comput. 18(3), 378–393 (2013)
Omidvar, M.N., Yang, M., Mei, Y., Li, X., Yao, X.: DG2: a faster and more accurate differential grou** for large-scale black-box optimization. IEEE Trans. Evol. Comput. 21(6), 929–942 (2017)
Yang, Z., Tang, K., Yao, X.: Self-adaptive differential evolution with neighborhood search. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1110–1116. IEEE (2008)
Liu, W., Zhou, Y., Li, B., Tang, K.: Cooperative co-evolution with soft grou** for large scale global optimization. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 318–325. IEEE (2019)
Li, C., Yang, S., Nguyen, T.T.: A self-learning particle swarm optimizer for global optimization problems. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(3), 627–646 (2011)
Cheng, R., **, Y.: A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191–204 (2014)
Tian, Y., Cheng, R., Zhang, X., **, Y.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization [educational forum]. IEEE Comput. Intell. Mag. 12(4), 73–87 (2017)
Neave, H., Worthington, P.: Distribution-free tests. Contemp. Sociol. 19(3), 488 (1990)
Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (Grant No. 71971142), and the Natural Science Foundation of Guangdong Province (No. 2022A1515010278, 2021A1515110595 and 2016A030310067).
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Chu, X., Pang, Y., Wang, J., Guo, Y., Qu, Y., Wang, Y. (2022). Data-Driven Recommendation Model with Meta-learning Autoencoder for Algorithm Selection. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_40
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DOI: https://doi.org/10.1007/978-981-19-6142-7_40
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