Abstract
Predicting the performance of an optimization algorithm on a new problem instance is crucial in order to select the most appropriate algorithm for solving that problem instance. For this purpose, recent studies learn a supervised machine learning (ML) model using a set of problem landscape features linked to the performance achieved by the optimization algorithm. However, these models are black-box with the only goal of achieving good predictive performance, without providing explanations which landscape features contribute the most to the prediction of the performance achieved by the optimization algorithm. In this study, we investigate the expressiveness of problem landscape features utilized by different supervised ML models in automated algorithm performance prediction. The experimental results point out that the selection of the supervised ML method is crucial, since different supervised ML regression models utilize the problem landscape features differently and there is no common pattern with regard to which landscape features are the most informative.
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References
Blot, A., Marmion, M., Jourdan, L., Hoos, H.H.: Automatic configuration of multi-objective local search algorithms for permutation problems. Evol. Comput. 27(1), 147–171 (2019). https://doi.org/10.1162/evco_a_00240
Eftimov, T., Jankovic, A., Popovski, G., Doerr, C., Korošec, P.: Personalizing performance regression models to black-box optimization problems. arXiv preprint arXiv:2104.10999 (2021)
Eftimov, T., Popovski, G., Renau, Q., Korošec, P., Doerr, C.: Linear matrix factorization embeddings for single-objective optimization landscapes. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 775–782. IEEE (2020)
Hansen, N., Auger, A., Ros, R., Mersmann, O., Tušar, T., Brockhoff, D.: COCO: a platform for comparing continuous optimizers in a black-box setting. Optim. Methods Softw. 36, 1–31 (2020)
Hutter, F., Kotthoff, L., Vanschoren, J. (eds.): Automated Machine Learning. TSSCML, Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5
Jankovic, A., Doerr, C.: Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2020), pp. 841–849. ACM (2020). https://doi.org/10.1145/3377930.3390183
Jankovic, A., Doerr, C.: Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 841–849 (2020)
Jankovic, A., Eftimov, T., Doerr, C.: Towards feature-based performance regression using trajectory data. In: Castillo, P.A., Jiménez Laredo, J.L. (eds.) EvoApplications 2021. LNCS, vol. 12694, pp. 601–617. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72699-7_38
Jankovic, A., Popovski, G., Eftimov, T., Doerr, C.: The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection. arXiv preprint arXiv:2104.09272 (2021)
Kerschke, P., Trautmann, H.: Automated algorithm selection on continuous black-box problems by combining exploratory landscape analysis and machine learning. Evol. Comput. 27(1), 99–127 (2019). https://doi.org/10.1162/evco_a_00236
Kerschke, P., Dagefoerde, J., Kerschke, M.P.: Package ‘flacco’ (2017)
Kerschke, P., Hoos, H.H., Neumann, F., Trautmann, H.: Automated algorithm selection: survey and perspectives. Evol. Comput. 27(1), 3–45 (2019)
Lang, R.D., Engelbrecht, A.P.: An exploratory landscape analysis-based benchmark suite. Algorithms 14(3), 78 (2021)
Liefooghe, A., Daolio, F., Vérel, S., Derbel, B., Aguirre, H.E., Tanaka, K.: Landscape-aware performance prediction for evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 24(6), 1063–1077 (2020). https://doi.org/10.1109/TEVC.2019.2940828
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 4765–4774. Curran Associates, Inc. (2017). http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., Rudolph, G.: Exploratory landscape analysis. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 829–836 (2011)
de Nobel, J., Vermetten, D., Wang, H., Doerr, C., Bäck, T.: Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules. CoRR abs/2102.12905 (2021). https://arxiv.org/abs/2102.12905
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Renau, Q., Doerr, C., Dreo, J., Doerr, B.: Exploratory landscape analysis is strongly sensitive to the sampling strategy. In: Bäck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 139–153. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58115-2_10
Renau, Q., Dreo, J., Doerr, C., Doerr, B.: Towards explainable exploratory landscape analysis: extreme feature selection for classifying BBOB functions. In: Castillo, P.A., Jiménez Laredo, J.L. (eds.) EvoApplications 2021. LNCS, vol. 12694, pp. 17–33. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72699-7_2
Škvorc, U., Eftimov, T., Korošec, P.: The effect of sampling methods on the invariance to function transformations when using exploratory landscape analysis. In: 2021 IEEE Congress on Evolutionary Computation (CEC), pp. 1139–1146. IEEE (2021)
Trajanov, R., Dimeski, S., Popovski, M., Korošec, P., Eftimov, T.: Explainable landscape-aware optimization performance prediction. arXiv preprint arXiv:2110.11633 (2021)
Trajanov, R., Dimeski, S., Popovski, M., Korošec, P., Eftimov, T.: GitHub repository containing all source code and data of the study presented in this paper (2021). https://github.com/risto-trajanov/explainable-landscape-aware-performance-regression
Xu, Q., Yang, Y., Liu, Y., Wang, X.: An improved Latin hypercube sampling method to enhance numerical stability considering the correlation of input variables. IEEE Access 5, 15197–15205 (2017)
Škvorc, U., Eftimov, T., Korošec, P.: Understanding the problem space in single-objective numerical optimization using exploratory landscape analysis. Appl. Soft Comput. 90, 106138 (2020). https://doi.org/10.1016/j.asoc.2020.106138. https://www.sciencedirect.com/science/article/pii/S1568494620300788
Acknowledgments
This work was supported by projects from the Slovenian Research Agency: research core funding No. P2-0098 and projects No. Z2-1867 and N2-0239.
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Trajanov, R., Dimeski, S., Popovski, M., Korošec, P., Eftimov, T. (2022). Explainable Landscape Analysis in Automated Algorithm Performance Prediction. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_14
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