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1. Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms

2. Runtime Analysis for State-of-the-Art Multi-objective Evolutionary Algorithms on the Subset Selection Problem

3. Proven Runtime Guarantees for How the MOEA/D: Computes the Pareto Front from the Subproblem Solutions

4. Learned Features vs. Classical ELA on Affine BBOB Functions

5. Hybridizing Target- and SHAP-Encoded Features for Algorithm Selection in Mixed-Variable Black-Box Optimization

6. Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler

7. Testing-Strategien

11. Zusammenfassung

12. Einleitung

13. Preventive Measures

21. Pattern-Based Information Flow Control for Safety-Critical On-Chip Systems

23. RF+clust for Leave-One-Problem-Out Performance Prediction

24. Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms

28. Summary

29. Introduction

32. Medienrecht

33. Deuterium

35. Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy

36. General Univariate Estimation-of-Distribution Algorithms

37. A First Runtime Analysis of the NSGA-II on a Multimodal Problem

38. High Dimensional Bayesian Optimization with Kernel Principal Component Analysis

39. Non-elitist Selection Can Improve the Performance of Irace

40. Improving Nevergrad’s Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration

41. Per-run Algorithm Selection with Warm-Starting Using Trajectory-Based Features

42. Agricultural Data Space

43. Data Usage Control

47. Stagnation Detection Meets Fast Mutation

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