Experimental Methods for the Analysis of Optimization Algorithms / Edition 1 available in Hardcover
- Pub. Date:
- Springer Berlin Heidelberg
In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods.
This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies.
This book is written for researchers and practitioners in operations research and computer science who wish to improve the experimental assessment of optimization algorithms and, consequently, their design.
|Publisher:||Springer Berlin Heidelberg|
|Product dimensions:||6.10(w) x 9.25(h) x 0.05(d)|
Table of Contents
Introduction.- Concepts and Practice of Algorithm Engineering.- Generating Experimental Data for Computational Testing in Scheduling Problems.- On the Performance Testing of Combinatorial Optimization Algorithms: The Scientific Method.- Algorithm Survival Analysis.- On Applications of Extreme Value Theory in Optimization.- F-Race and Further Enhancements.- Comparing the Performance of Evolutionary Algorithms with Multiple Hypothesis Testing.- Mixed Models for the Analysis of Local Search Components.- Sequential Experiment Designs for Screening and Tuning Parameters of Stochastic Heuristics.- Sequential Parameter Optimization (SPO) and the Role of Tuning in Experimental Analysis.- An Overview of the Design and Analysis of Simulation Experiments for Sensitivity Analysis.- The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison.- Experimental Analysis of Stochastic Local Search Components for Multiobjective Problems.- An Introduction to Inferential Statistics.