Automatically Improving the Anytime Behaviour of Optimisation Algorithms: Supplementary material

Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled also as a set of mutually nondominated, bi-objective points. Using this model, we propose to combine an automatic configuration tool and the hypervolume measure, which assigns a single quality measure to a nondominated set. Our goal is to improve the anytime behaviour of optimisation algorithms by means of automatically finding algorithmic configurations that produce the best nondominated sets. Moreover, the recently proposed weighted hypervolume measure is used here to incorporate the decision-maker’s preferences into the automatic tuning procedure. We report on the improvements reached when applying the proposed method to two relevant scenarios: (i) the design of parameter variation strategies for MAX-MIN Ant System, and (ii) the tuning of the anytime behaviour of SCIP, an open-source mixed integer programming solver with more than 200 parameters. Paper extended version http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2012-012.pdf TSP instances http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/ SCIP instances http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/results.html Please cite this article as: López-Ibáñez, M., & Stützle, T. Automatically improving the anytime behaviour of optimisation algorithms. European Journal of Operational Research, 235(3): 569-582 (2014), doi: 10.1016/j.ejor.2013.10.043 ∗Corresponding author Email addresses: [email protected] (Manuel López-Ibáñez), [email protected] (Thomas Stützle) Please cite this article as: López-Ibáñez, M., & Stützle, T. Automatically improving the anytime behaviour of optimisation algorithms. European Journal of Operational Research, 235(3): 569-582 (2014), doi: 10.1016/j.ejor.2013.10.043 ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● default whv (xqual) auto anytime