no code implementations • 8 Apr 2024 • Quentin Renau, Emma Hart
The algorithm-configuration tool irace is used to tune the parameters of a simple Simulated Annealing algorithm (SA) to produce trajectories that maximise the performance metrics of ML models trained on this data.
no code implementations • 23 Jan 2024 • Quentin Renau, Emma Hart
Input data can take the form of features derived from the instance description or fitness landscape, or can be a direct representation of the instance itself, i. e. an image or textual description.
no code implementations • 7 May 2022 • Quentin Renau, Johann Dreo, Alain Peres, Yann Semet, Carola Doerr, Benjamin Doerr
The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed.
no code implementations • 1 Feb 2021 • Quentin Renau, Johann Dreo, Carola Doerr, Benjamin Doerr
We show that the classification accuracy transfers to settings in which several instances are involved in training and testing.
no code implementations • 30 Sep 2020 • Tome Eftimov, Gorjan Popovski, Quentin Renau, Peter Korosec, Carola Doerr
Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning, and TSP.
no code implementations • 19 Jun 2020 • Quentin Renau, Carola Doerr, Johann Dreo, Benjamin Doerr
While, not unexpectedly, increasing the number of sample points gives more robust estimates for the feature values, to our surprise we find that the feature value approximations for different sampling strategies do not converge to the same value.