Kerschke, Pascal: Automated and Feature-Based Problem Characterization and Algorithm Selection Through Machine Learning
Inhalt
- Abstract
- Introduction
- Characterizing the Global Structure of Continuous Black-Box Problems
- Contributed Material
- Cell Mapping Techniques for Exploratory Landscape Analysis
- Detecting Funnel Structures by Means of Exploratory Landscape Analysis
- Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models
- Flacco – A Toolbox for Exploratory Landscape Analysis with R
- Contributed Material
- The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems
- flaccogui: Exploratory Landscape Analysis for Everyone
- Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco
- Feature-Based Algorithm Selection from Optimizer Portfolios
- Contributed Material
- Improving the State of the Art in Inexact TSP Solving using Per-Instance Algorithm Selection
- Leveraging TSP Solver Complementarity through Machine Learning
- Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning
- Platforms for Collaborative Research on Algorithm Selection and Machine Learning
- Contributed Material
- ASlib: A Benchmark Library for Algorithm Selection
- OpenML: An R Package to Connect to the Machine Learning Platform OpenML
- Summary and Outlook
- Bibliography
- Appendix: Contributed Publications
- Characterizing the Global Structure of Continuous Black-Box Problems
- Cell Mapping Techniques for Exploratory Landscape Analysis
- Detecting Funnel Structures By Means of Exploratory Landscape Analysis
- Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models
- Flacco – A Toolbox for Exploratory Landscape Analysis with R
- The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems
- flaccogui: Exploratory Landscape Analysis for Everyone
- Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco
- Feature-Based Algorithm Selection from Optimizer Portfolios
- Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection
- Leveraging TSP Solver Complementarity through Machine Learning
- Automated Algorithm Selection on Continuous Black-Box Problems By Combining Exploratory Landscape Analysis and Machine Learning
- Platforms for Collaborative Research on Algorithm Selection and Machine Learning