Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Dietrich, Konstantin, Prager, Raphael Patrick, Doerr, Carola, Trautmann, Heike |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Exploratory Landscape Analysis for Mixed-Variable Problems
by: Prager, Raphael Patrick, et al.
Published: (2024)
by: Prager, Raphael Patrick, et al.
Published: (2024)
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
by: Dietrich, Konstantin, et al.
Published: (2024)
by: Dietrich, Konstantin, et al.
Published: (2024)
Similarity-based Portfolio Construction for Black-box Optimization
by: Dinu, Catalin-Viorel, et al.
Published: (2026)
by: Dinu, Catalin-Viorel, et al.
Published: (2026)
Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms
by: López-Ibáñez, Manuel, et al.
Published: (2024)
by: López-Ibáñez, Manuel, et al.
Published: (2024)
How Sequential Algorithm Portfolios can benefit Black Box Optimization
by: Dinu, Catalin-Viorel, et al.
Published: (2026)
by: Dinu, Catalin-Viorel, et al.
Published: (2026)
MO-ELA: Rigorously Expanding Exploratory Landscape Features for Automated Algorithm Selection in Continuous Multi-Objective Optimisation
by: Preuß, Oliver, et al.
Published: (2026)
by: Preuß, Oliver, et al.
Published: (2026)
When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection
by: Antipov, Denis, et al.
Published: (2026)
by: Antipov, Denis, et al.
Published: (2026)
MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler
by: Vermetten, Diederick, et al.
Published: (2024)
by: Vermetten, Diederick, et al.
Published: (2024)
On the Structural (Dis)Agreement of Landscape Representations in Black-Box Optimization
by: Gjorgjieva, Sara, et al.
Published: (2026)
by: Gjorgjieva, Sara, et al.
Published: (2026)
Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear Functions
by: Doerr, Carola, et al.
Published: (2023)
by: Doerr, Carola, et al.
Published: (2023)
Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics
by: Doerr, Benjamin, et al.
Published: (2024)
by: Doerr, Benjamin, et al.
Published: (2024)
Finding Low Star Discrepancy 3D Kronecker Point Sets Using Algorithm Configuration Techniques
by: Abderrahim, Imène Ait, et al.
Published: (2026)
by: Abderrahim, Imène Ait, et al.
Published: (2026)
Enhancing Parameter Control Policies with State Information
by: Covini, Gianluca, et al.
Published: (2025)
by: Covini, Gianluca, et al.
Published: (2025)
Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks
by: Nikolikj, Ana, et al.
Published: (2024)
by: Nikolikj, Ana, et al.
Published: (2024)
Large-scale Benchmarking of Metaphor-based Optimization Heuristics
by: Vermetten, Diederick, et al.
Published: (2024)
by: Vermetten, Diederick, et al.
Published: (2024)
Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms
by: Wietheger, Simon, et al.
Published: (2024)
by: Wietheger, Simon, et al.
Published: (2024)
Evolutionary Algorithms Are Significantly More Robust to Noise When They Ignore It
by: Antipov, Denis, et al.
Published: (2024)
by: Antipov, Denis, et al.
Published: (2024)
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
by: Nikolikj, Ana, et al.
Published: (2023)
by: Nikolikj, Ana, et al.
Published: (2023)
Runtime Analysis of the SMS-EMOA for Many-Objective Optimization
by: Zheng, Weijie, et al.
Published: (2023)
by: Zheng, Weijie, et al.
Published: (2023)
Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes Benchmark
by: Chwiałkowski, Marcel, et al.
Published: (2025)
by: Chwiałkowski, Marcel, et al.
Published: (2025)
Runtime Analysis for Multi-Objective Evolutionary Algorithms in Unbounded Integer Spaces
by: Doerr, Benjamin, et al.
Published: (2024)
by: Doerr, Benjamin, et al.
Published: (2024)
Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables
by: Jedidia, Firas Ben, et al.
Published: (2023)
by: Jedidia, Firas Ben, et al.
Published: (2023)
The First Theoretical Approximation Guarantees for the Non-Dominated Sorting Genetic Algorithm III (NSGA-III)
by: Deng, Renzhong, et al.
Published: (2025)
by: Deng, Renzhong, et al.
Published: (2025)
Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler
by: Vermetten, Diederick, et al.
Published: (2024)
by: Vermetten, Diederick, et al.
Published: (2024)
Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks
by: Nikolikj, Ana, et al.
Published: (2024)
by: Nikolikj, Ana, et al.
Published: (2024)
Improved Runtime Guarantees for the SPEA2 Multi-Objective Optimizer
by: Doerr, Benjamin, et al.
Published: (2025)
by: Doerr, Benjamin, et al.
Published: (2025)
Cascading CMA-ES Instances for Generating Input-diverse Solution Batches
by: Santoni, Maria Laura, et al.
Published: (2025)
by: Santoni, Maria Laura, et al.
Published: (2025)
On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+($λ$,$λ$))-GA
by: Nguyen, Tai, et al.
Published: (2025)
by: Nguyen, Tai, et al.
Published: (2025)
On the Influence of the Feature Computation Budget on Per-Instance Algorithm Selection for Black-Box Optimization
by: van der Blom, Koen, et al.
Published: (2026)
by: van der Blom, Koen, et al.
Published: (2026)
First Mathematical Runtime Analyses of Multi-Objective Evolutionary Algorithms for Multi-Valued Decision Variables
by: Li, Mingfeng, et al.
Published: (2026)
by: Li, Mingfeng, et al.
Published: (2026)
LLM Driven Design of Continuous Optimization Problems with Controllable High-level Properties
by: Skvorc, Urban, et al.
Published: (2026)
by: Skvorc, Urban, et al.
Published: (2026)
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
by: Huang, Beichen, et al.
Published: (2024)
by: Huang, Beichen, et al.
Published: (2024)
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization
by: Yang, Xu, et al.
Published: (2025)
by: Yang, Xu, et al.
Published: (2025)
Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives
by: Zheng, Weijie, et al.
Published: (2020)
by: Zheng, Weijie, et al.
Published: (2020)
Approximation Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)
by: Zheng, Weijie, et al.
Published: (2022)
by: Zheng, Weijie, et al.
Published: (2022)
Proven Approximation Guarantees in Multi-Objective Optimization: SPEA2 Beats NSGA-II
by: Alghouass, Yasser, et al.
Published: (2025)
by: Alghouass, Yasser, et al.
Published: (2025)
Continuous signal sparse encoding using analog neuromorphic variability
by: Costa, Filippo, et al.
Published: (2025)
by: Costa, Filippo, et al.
Published: (2025)
Multi-parameter Control for the $(1+(λ,λ))$-GA on OneMax via Deep Reinforcement Learning
by: Nguyen, Tai, et al.
Published: (2025)
by: Nguyen, Tai, et al.
Published: (2025)
A Survey of Meta-features Used for Automated Selection of Algorithms for Black-box Single-objective Continuous Optimization
by: Cenikj, Gjorgjina, et al.
Published: (2024)
by: Cenikj, Gjorgjina, et al.
Published: (2024)
Self-Adjusting Evolutionary Algorithms Are Slow on Multimodal Landscapes
by: Lengler, Johannes, et al.
Published: (2024)
by: Lengler, Johannes, et al.
Published: (2024)
Similar Items
-
Exploratory Landscape Analysis for Mixed-Variable Problems
by: Prager, Raphael Patrick, et al.
Published: (2024) -
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization
by: Dietrich, Konstantin, et al.
Published: (2024) -
Similarity-based Portfolio Construction for Black-box Optimization
by: Dinu, Catalin-Viorel, et al.
Published: (2026) -
Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms
by: López-Ibáñez, Manuel, et al.
Published: (2024) -
How Sequential Algorithm Portfolios can benefit Black Box Optimization
by: Dinu, Catalin-Viorel, et al.
Published: (2026)