no code implementations • 9 Sep 2022 • Risto Trajanov, Ana Nikolikj, Gjorgjina Cenikj, Fabien Teytaud, Mathurin Videau, Olivier Teytaud, Tome Eftimov, Manuel López-Ibáñez, Carola Doerr
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc.
no code implementations • 22 Mar 2022 • Risto Trajanov, Stefan Dimeski, Martin Popovski, Peter Korošec, Tome Eftimov
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.
1 code implementation • 22 Oct 2021 • Risto Trajanov, Stefan Dimeski, Martin Popovski, Peter Korošec, Tome Eftimov
In this study, we are investigating explainable landscape-aware regression models where the contribution of each landscape feature to the prediction of the optimization algorithm performance is estimated on a global and local level.
