Abstract
This paper addresses the problem of tuning parameters of mathematical solvers to increase their performance. We investigate how solvers can be tuned for models that undergo two types of configuration: variable configuration and constraint configuration. For each type, we investigate search algorithms for data generation that emphasizes exploration or exploitation. We show the difficulties for solver tuning in constraint configuration and how data generation methods affects a training sets learning potential.
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References
Barry, M., Schillinger, M., Weigt, H., Schumann, R.: Configuration of hydro power plant mathematical models. In: Gottwalt, S., König, L., Schmeck, H. (eds.) EI 2015. LNCS, vol. 9424, pp. 200–207. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25876-8_17
Barry, M., Schumann, R.: Dynamic and configurable mathematical modelling of a hydropower plant research in progress paper. In: Presented at the 29. Workshop “Planen, Scheduling und Konfigurieren, Entwerfen” (PuK 2015), September 2015
Baz, M., Hunsaker, B., Brooks, P., Gosavi, A.: Automated tuning of optimization software parameters. Technical Report TR2007-7. University of Pittsburgh, Department of Industrial Engineering (2007)
Baz, M., Hunsaker, B., Prokopyev, O.: How much do we “pay” for using default parameters? Comput. Optim. Appl. 48(1), 91–108 (2011)
Boussaa, M., Barais, O., Sunyé, G., Baudry, B.: A novelty search approach for automatic test data generation. In: Proceedings of the Eighth International Workshop on Search-Based Software Testing, pp. 40–43. IEEE Press (2015)
Chawdhry, P.K., Roy, R., Pant, R.K.: Soft Computing in Engineering Design and Manufacturing. Springer, London (2012)
Cplex, G.: The solver manuals (2014)
Drud, A.: Conopt solver manual. ARKI Consulting and Development, Bagsvaerd, Denmark (1996)
Guo, H., Viktor, H.L.: Learning from imbalanced data sets with boosting and data generation: the databoost-IM approach. ACM SIGKDD Explor. Newsl. 6(1), 30–39 (2004)
Gurobi Optimization, Inc.: Gurobi optimizer reference manual (2016). http://www.gurobi.com
He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Automated configuration of mixed integer programming solvers. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 186–202. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13520-0_23
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: Paramils: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267–306 (2009)
Hutter, F., Xu, L., Hoos, H.H., Leyton-Brown, K.: Algorithm runtime prediction: methods & evaluation. Artif. Intell. 206, 79–111 (2014)
IBM: CPLEX Performance Tuning for Mixed Integer Programs (2016). http://www-01.ibm.com/support/docview.wss?uid=swg21400023
Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (1996)
Juslin, P., Winman, A., Olsson, H.: Naive empiricism and dogmatism in confidence research: a critical examination of the hard-easy effect. Psychol. Rev. 107(2), 384 (2000)
Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC-instance-specific algorithm configuration. In: ECAI, vol. 215, pp. 751–756 (2010)
Karlik, B., Olgac, A.V.: Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 1(4), 111–122 (2011)
Knowles, J.: Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evol. Comput. 10(1), 50–66 (2006)
Kotthoff, L.: Algorithm selection for combinatorial search problems: a survey. AI Mag. 35(3), 48–60 (2014)
Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19(2), 189–223 (2011)
Lehmann, G., Blumendorf, M., Trollmann, F., Albayrak, S.: Meta-modeling runtime models. In: Dingel, J., Solberg, A. (eds.) MODELS 2010. LNCS, vol. 6627, pp. 209–223. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21210-9_21
López-Ibánez, M., Stützle, T.: Automatically improving the anytime behaviour of optimisation algorithms. Eur. J. Oper. Res. 235(3), 569–582 (2014)
Preuss, M., Rudolph, G., Wessing, S.: Tuning optimization algorithms for real-world problems by means of surrogate modeling. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 401–408. ACM (2010)
Stefan Eggenschwiler, R.S.: Parameter tuning for the CPLEX. Bachelor Thesis (2016)
Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Hydra-MIP: automated algorithm configuration and selection for mixed integer programming. In: RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion at the International Joint Conference on Artificial Intelligence (IJCAI), pp. 16–30 (2011)
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Parts of this work have been funded by the Swiss National Science Foundation as part of the project 407040_153760 Hydro Power Operation and Economic Performance in a Changing Market Environment.
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Barry, M., Abgottspon, H., Schumann, R. (2018). Solver Tuning and Model Configuration. In: Trollmann, F., Turhan, AY. (eds) KI 2018: Advances in Artificial Intelligence. KI 2018. Lecture Notes in Computer Science(), vol 11117. Springer, Cham. https://doi.org/10.1007/978-3-030-00111-7_13
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DOI: https://doi.org/10.1007/978-3-030-00111-7_13
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