Name
Title | ||
|---|---|---|
Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. |
Abstract | ||
|---|---|---|
In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-14714-2_1 | Parallel Problem Solving from Nature |
Keywords | DocType | Citations |
Automated algorithm selection,Exploratory landscape analysis,Deep learning,Continuous optimization | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raphael Patrick Prager | 1 | 0 | 1.69 |
| Seiler Moritz Vinzent | 2 | 0 | 0.34 |
| Heike Trautmann | 3 | 623 | 43.22 |
| pascal kerschke | 4 | 118 | 13.55 |