Experimental Methods for the Analysis of Optimization Algorithms

1. Introduction
Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss

2. The Future of Experimental Research
Thomas Bartz-Beielstein, Mike Preuss

3. Design and Analysis of Computational Experiments: Overview
Jack P.C. Kleijnen

4. The Generation of Experimental Data for Computational Testing in Optimization
Nicholas G. Hall, Marc E. Posner

5. The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison
Viviane Grunert Fonseca, Carlos M. Fonseca

6. Algorithm Engineering: Concepts and Practice
Markus Chimani, Karsten Klein

7. Algorithm Survival Analysis
Matteo Gagliolo, Catherine Legrand

8. On Applications of Extreme Value Theory in Optimization
Jürg Hüsler

9. Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization
Manuel López-Ibáñez, Luís Paquete, Thomas Stützle

10. Mixed Models for the Analysis of Optimization Algorithms
Marco Chiarandini, Yuri Goegebeur

11. Tuning an Algorithm Using Design of Experiments
Enda Ridge, Daniel Kudenko

12. Using Entropy for Parameter Analysis of Evolutionary Algorithms
Selmar K. Smit, Agoston E. Eiben

13.
F-Race and Iterated F-Race: An Overview
Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, Thomas Stützle

14. The Sequential Parameter Optimization Toolbox
Thomas Bartz-Beielstein, Christian Lasarczyk, Mike Preuss

15. Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches
Frank Hutter, Thomas Bartz-Beielstein, Holger H. Hoos, Kevin Leyton-Brown, Kevin P. Murphy

Keywords: Computer Science, Probability and Statistics in Computer Science, Operations Research, Mathematical Programming, Algorithms, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Complexity