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Found 6 papers, 0 papers with code
Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples
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.
On the Utility of Probing Trajectories for Algorithm-Selection
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.
Automated Algorithm Selection for Radar Network Configuration
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.
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
We show that the classification accuracy transfers to settings in which several instances are involved in training and testing.
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes
Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning, and TSP.
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy
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.
