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Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples

Renau, Quentin; Hart, Emma



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 have shown promise. However, it is unclear to what extent these trajectories reliably discriminate between solvers. We propose a meta approach to generating discriminatory trajectories with respect to a portfolio of solvers. 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. We show that when the trajectories obtained from the tuned SA algorithm are used in ML models for algorithm-selection and performance prediction, we obtain significantly improved performance metrics compared to models trained both on raw trajectory data and on exploratory landscape features.


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

Presentation Conference Type Conference Paper (published)
Conference Name GECCO 2024
Start Date Jul 14, 2024
End Date Jul 18, 2024
Acceptance Date Mar 21, 2024
Deposit Date May 2, 2024
Publisher Association for Computing Machinery (ACM)
Peer Reviewed Peer Reviewed
Keywords Algorithm Selection; Performance Prediction; Black-Box Optimisa- tion; Algorithm Trajectory

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