Abstract
In various domains, engineers face the challenge of optimising system configurations while considering numerous constraints. A common goal is not to identify the best configuration as fast as possible, but rather to find a useful set of very good configurations in a given time for further elaboration by human engineers. Existing techniques for exploring large configuration spaces work well on Euclidean configuration spaces (e.g., with Boolean and numerical configuration decisions). However, it is unclear to what extent they are applicable to configuration problems where solutions are represented as graphs – a common representation in many engineering disciplines. To investigate this problem, we propose an adaptation of existing techniques for Euclidean configurations, to graph configuration spaces by applying graph embedding. We demonstrate the feasibility of this adapted pipeline and conduct a controlled experiment to estimate its efficiency. We apply our approach to a sample case of HVAC (Heating, Ventilation, and Air-Conditioning) systems in 40,000 simulated houses. By first learning the configuration space from a small number of simulations, we can identify 75% of the best configurations within 7,508 simulations compared to 29,725 simulations without our approach. That is a speed-up of 4.0\(\times \) and saves more than 15 days if one simulation takes about one minute, as in our experimental set-up.
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References
Building Ventilation - MATLAB Simulink. https://www.mathworks.com/help/simscape/ug/building-ventilation.html. Accessed 10 May 2023
MATLAB Simulink - Simulation and Model-Based Design. https://uk.mathworks.com/products/simulink.html. Accessed 10 May 2023
https://github.com/mittermm/LEGCS. Accessed 10 May 2023
Acher, M., et al.: Learning very large configuration spaces: what matters for linux kernel sizes. Inria Rennes-Bretagne Atlantique. hal-02314830 (2019)
Cai, C., Wang, Y.: A simple yet effective baseline for non-attributed graph classification. arXiv:1811.03508 (2022)
Cai, H., Zheng, V.W., Chang, K.C.C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)
Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., Murphy, K.: Machine learning on graphs: a model and comprehensive taxonomy. J. Mach. Learn. Res. 23, 89:1–89:64 (2022)
Chica, M., Juan, A.A., Bayliss, C., Cordon, O., Kelton, D.: Why simheuristics? benefits, limitations, and best practices when combining metaheuristics with simulation. In: Statistics and Operations Research Transactions (2020)
Church, R.L., Baez, C.A.: Generating optimal and near-optimal solutions to facility location problems. Environ. Plan. B: Urban Anal. City Sci. 47(6), 1014–1030 (2020)
Easterbrook, S., Singer, J., Storey, M.A.D., Damian, D.E.: Selecting empirical methods for software engineering research. In: Guide to Advanced Empirical Software Engineering, pp. 285–311. Springer, Heidelberg (2008). https://doi.org/10.1007/978-1-84800-044-5_11
Eppstein, D., Kurz, D.: \( k \)-best solutions of MSO problems on tree-decomposable graphs. In: 12th International Symposium on Parameterized and Exact Computation (IPEC) (2017)
Farahani, R.Z., Miandoabchi, E., Szeto, W.Y., Rashidi-Bajgan, H.: A review of urban transportation network design problems. Eur. J. Oper. Res. 229(2), 281–302 (2013)
Glinz, M.: On non-functional requirements. In: International Requirements Engineering Conference (RE), pp. 21–26 (2007)
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl. Based Syst. 151, 78–94 (2018)
Jedlitschka, A., Ciolkowski, M., Pfahl, D.: Reporting experiments in software engineering. In: Guide to Advanced Empirical Software Engineering, pp. 201–228. Springer, Heidelberg (2008)
Juan, A.A., Kelton, W.D., Currie, C.S.M., Faulin, J.: Simheuristics applications: dealing with uncertainty in logistics, transportation, and other supply chain areas. In: WSC, pp. 3048–3059. IEEE (2018)
Karunasingha, D.S.K.: Root mean square error or mean absolute error? use their ratio as well. Inf. Sci. 585, 609–629 (2022)
Kriege, N.M., Johansson, F.D., Morris, C.: A survey on graph kernels. Appl. Netw. Sci. 5(1), 6 (2020)
Liaw, A., Wiener, M.: Classification and Regression by randomForest. R News 2(3), 18–22 (2002)
Lopes, R., Tutenel, T., Smelik, R.M., De Kraker, K.J., Bidarra, R.: A constrained growth method for procedural floor plan generation. In: Proceedings of International Conference on Intelligent Games Simulation, pp. 13–20 (2010)
Makarov, I., Kiselev, D., Nikitinsky, N., Subelj, L.: Survey on graph embeddings and their applications to machine learning problems on graphs. PeerJ Comput. Sci. 7, e357 (2021)
Miao, S., Liu, M., Li, P.: Interpretable and generalizable graph learning via stochastic attention mechanism. In: International Conference on Machine Learning, pp. 15524–15543. PMLR (2022)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Peng, Y., Choi, B., Jianliang, X.: Graph learning for combinatorial optimization: a survey of state-of-the-art. Data Sci. Eng. 6(2), 119–141 (2021)
Pereira, J.A., Acher, M., Martin, H., Jézéquel, J.M., Botterweck, G., Ventresque, A.: Learning software configuration spaces: a systematic literature review. J. Syst. Softw. 182, 111044 (2021)
Pienta, R.S., Abello, J., Kahng, M., Chau, D.H.: Scalable graph exploration and visualization: sensemaking challenges and opportunities. In: International Conference on Big Data and Smart Computing (BIGCOMP), pp. 271–278. IEEE Computer Society (2015)
Rozemberczki, B., Kiss, O., Sarkar, R.: Karate club: an API oriented open-source python framework for unsupervised learning on graphs. In: International Conference on Information and Knowledge Management (CIKM), pp. 3125–3132. ACM (2020)
Shaw, M.: Writing good software engineering research paper. In Proceedings of the 25th International Conference on Software Engineering, pp. 726–737. IEEE Computer Society (2003)
Siegmund, N., et al.: Scalable prediction of non-functional properties in software product lines. In: SPLC (2011)
Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., Philip, S.Y.: A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Trans. Big Data 9(2), 415–436 (2022)
Zhou, J., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020)
Acknowledgement
This work was supported by Science Foundation Ireland grant 13/RC/2094_P2 to Lero - the Science Foundation Ireland Research Centre for Software (www.lero.ie).
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Mittermaier, M., Saber, T., Botterweck, G. (2024). Learning Graph Configuration Spaces with Graph Embedding in Engineering Domains. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14506. Springer, Cham. https://doi.org/10.1007/978-3-031-53966-4_25
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