Dr Quentin Renau Q.Renau@napier.ac.uk
Research Fellow
Dr Quentin Renau Q.Renau@napier.ac.uk
Research Fellow
Prof Emma Hart E.Hart@napier.ac.uk
Professor
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-Selectors and Performance-Predictors via Learning Discriminating Training Samples. Presented at GECCO 2024, Melbourne, Australia
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 |
Online Publication Date | Jul 14, 2024 |
Publication Date | 2024 |
Deposit Date | May 2, 2024 |
Publicly Available Date | Jul 14, 2024 |
Publisher | Association for Computing Machinery (ACM) |
Peer Reviewed | Peer Reviewed |
Pages | 1026 - 1035 |
Book Title | GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference |
ISBN | 9798400704949 |
DOI | https://doi.org/10.1145/3638529.3654025 |
Keywords | Algorithm Selection; Performance Prediction; Black-Box Optimisa- tion; Algorithm Trajectory |
Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples
(958 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
On the Utility of Probing Trajectories for Algorithm-Selection
(2024)
Presentation / Conference Contribution
Towards optimisers that `Keep Learning'
(2023)
Presentation / Conference Contribution
Ealain: A Camera Simulation Tool to Generate Instances for Multiple Classes of Optimisation Problem
(2024)
Presentation / Conference Contribution
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances
(2024)
Presentation / Conference Contribution
An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation
(2025)
Presentation / Conference Contribution
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search