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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References
Chu, X., et al.: Learning–interaction–diversification framework for swarm intelligence optimizers: a unified perspective. Neural Comput. Appl. 32(6), 1789–1809 (2018)
Wolpert, D.H.a.M., W.G.: No free lunch theorems for optimization.pdf. IEEE Trans. Evol. Comput. 1, 67–82 (1997)
Kerschke, P., et al.: Automated algorithm selection: survey and perspectives. Evol. Comput. 27(1), 3–45 (2019)
Lemke, C., Budka, M., Gabrys, B.: Metalearning: a survey of trends and technologies. Artif. Intell. Rev. 44(1), 117–130 (2015)
Smith-Miles, K.A.: IEEE. Towards Insightful Algorithm Selection For Optimisation Using Meta-Learning Concepts. International Joint Conference on Neural Networks, pp. 4118–4124. Hong Kong, PEOPLES R CHINA (2008)
Chu, X., et al.: Adaptive recommendation model using meta-learning for population-based algorithms. Inf. Sci. 476, 192–210 (2019)
Dantas, A.L., Pozo, A.T.R.: IEEE. A meta- learning algorithm selection approach for the quadratic assignment problem. IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), pp. 1284–1291. Rio de Janeiro, BRAZIL (2018)
Cui, C., et al.: A recommendation system for meta-modeling: A meta-learning based approach. Expert Syst. Appl. 46, 33–44 (2016)
Peng, Y.H., et al.: Improved dataset characterisation for meta-learning (2002)
Chu, X., et al.: Empirical study on meta-feature characterization for multi-objective optimization problems. Neural Comput. Appl. 34(19), 16255–16273 (2022)
Rice, J.R.: The Algorithm Selection Problem (1976)
Khan, I., et al.: A literature survey and empirical study of meta-learning for classifier selection. IEEE Access 8, 10262–10281 (2020)
Wang, G.T., et al.: A generic multilabel learning-based classification algorithm recommendation method. ACM Trans. Knowl. Discovery Data 9(1) (2014)
Rossi, A.L.D., et al.: MetaStream: a meta-learning based method for periodic algorithm selection in time-changing data. Neurocomputing 127, 52–64 (2014)
Muñoz, M.A., Kirley, M., Halgamuge, S.K.: The algorithm selection problem on the continuous optimization domain. In: Moewes, C., Nürnberger, A. (eds.) Computational Intelligence in Intelligent Data Analysis, pp. 75–89. Springer Berlin Heidelberg, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-32378-2_6
Takahashi, R., et al.: Recommendation of web service selection algorithm based on web application review. IEEE-Region-10 Conference (IEEE TENCON), pp. 1882–1887. IEEE Reg 10, SOUTH KOREA (2018)
Degroote, H.: Online Algorithm Selection. 26th International Joint Conference on Artificial Intelligence (IJCAI), pp. 5173–5174. Melbourne, AUSTRALIA (2017)
Misir, M., Gunawan, A., Vansteenwegen, P.: Algorithm selection for the team orienteering problem. In: 22nd European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP) Held as Part of EvoStar Conference. 13222, pp. 33–45. Madrid, SPAIN (2022)
Baldi, P., Hornik, K.: Neural networks and principal component analysis: learning from examples without local minima. Neural Netw. 2(1), 53–58 (1989)
Liu, G., Bao, H., Han, B.: A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis. Math. Probl. Eng. 2018, 1–10 (2018)
Wang, L., et al.: A computational-based method for predicting drug-target interactions by using stacked autoencoder deep neural network. J. Comput. Biol. 25(3), 361–373 (2018)
Chen, Y., et al.: deep learning-based classification of hyperspectral data. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 7(6), 2094–2107 (2014)
Tao, S., et al.: Bearing fault diagnosis method based on stacked autoencoder and softmax regression. In: 2015 34th Chinese Control Conference (CCC), pp. 6331–6335. IEEE (2015)
Smith-Miles, K.A.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 1–25 (2009)
Ferrari, D.G., de Castro, L.N.: Clustering algorithm selection by meta-learning systems: a new distance-based problem characterization and ranking combination methods. Inf. Sci. 301, 181–194 (2015)
Tang, K., et al.: Benchmark functions for the CEC’2008 special session and competition on large scale global optimization. Nat. Inspired Comput. Appl. Lab. USTC, China 24, 1–18 (2007)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)
Cheng, R., **, Y.: A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191–204 (2014)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press (1992)
Noel, M.M.: A new gradient based particle swarm optimization algorithm for accurate computation of global minimum. Appl. Soft Comput. 12(1), 353–359 (2012)
Sallam, K.M., et al.: Improved multi-operator differential evolution algorithm for solving unconstrained problems. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)
Zhu, G.-Y., Zhang, W.-B.: Optimal foraging algorithm for global optimization. Appl. Soft Comput. 51, 294–313 (2017)
Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation, pp. 71–78. IEEE (2013)
Tian, Y., et al.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization [educational forum]. IEEE Comput. Intell. Mag. 12(4), 73–87 (2017)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Gulli, A.: and S. Packt Publishing Ltd, Pal. Deep learning with Keras (2017)
Neave, H.R., Worthington, P.L.: Distribution-Free Tests. Unwin Hyman, (1988)
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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