All Outputs (2)

On the Utility of Probing Trajectories for Algorithm-Selection (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, April). On the Utility of Probing Trajectories for Algorithm-Selection. Presented at EvoStar 2024, Aberystwyth, UK

Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance description or fitness landscape , or can be a direct representation of the ins... Read More about On the Utility of Probing Trajectories for Algorithm-Selection.

Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, July). Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples. Presented at GECCO 2024, Melbourne, USA

The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajec-tories obtained from running a solver as input... Read More about Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples.