Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods

Prager, Raphael Patrick; Seiler, Michael P.; Trautmann, Heike; Kerschke, Pascal

doi:10.1007/978-3-031-14714-2_1

Cited by 8 publications

(2 citation statements)

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“…Our study demonstrated that performance prediction models built on ELA features effectively generalize across the three tested algorithms. Next, we will explore additional feature landscape meta-features, such as topological features [39] and those derived from deep neural network architectures [40], comparing them with ELA features to enhance predictive accuracy. Finally, we aim to evaluate these measures in an active learning setting, using them to determine if a model is suitable for new instances or if further training and fine-tuning are necessary.…”

Section: Discussionmentioning

confidence: 99%

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Assessing the Generalizability of a Performance Predictive Model

Nikolikj

Cenikj

Ispirova

et al. 2023

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

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A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model. The predictive model uses the feature representation of a set of problem instances as input data and predicts the algorithm performance achieved on them. Common machine learning models struggle to make predictions for instances with feature representations not covered by the training data, resulting in poor generalization to unseen problems. In this study, we propose a workflow to estimate the generalizability of a predictive model for algorithm performance, trained on one benchmark suite to another. The workflow has been tested by training predictive models across benchmark suites and the results show that generalizability patterns in the landscape feature space are reflected in the performance space.CCS Concepts: • Computing methodologies → Machine learning; Learning latent representations; Supervised learning; • Theory of computation → Design and analysis of algorithms.

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Section: Discussionmentioning

confidence: 99%

“…In the future, we aim to identify specific meta-features tailored to individual algorithms or their respective families. This could involve conducting feature selection on the ELA features or creating and assessing alternative landscape features [39], [40].…”

Section: A First Experimentsmentioning

confidence: 99%