Ebook: Experimental Methods for the Analysis of Optimization Algorithms

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.

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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.

---

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.

Content:
Front Matter....Pages i-xxii
Introduction....Pages 1-13
Front Matter....Pages 15-15
The Future of Experimental Research....Pages 17-49
Design and Analysis of Computational Experiments: Overview....Pages 51-72
The Generation of Experimental Data for Computational Testing in Optimization....Pages 73-101
The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison....Pages 103-130
Algorithm Engineering: Concepts and Practice....Pages 131-158
Front Matter....Pages 159-159
Algorithm Survival Analysis....Pages 161-184
On Applications of Extreme Value Theory in Optimization....Pages 185-207
Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization....Pages 209-222
Front Matter....Pages 223-223
Mixed Models for the Analysis of Optimization Algorithms....Pages 225-264
Tuning an Algorithm Using Design of Experiments....Pages 265-286
Using Entropy for Parameter Analysis of Evolutionary Algorithms....Pages 287-310
F-Race and Iterated F-Race: An Overview....Pages 311-336
The Sequential Parameter Optimization Toolbox....Pages 337-362
Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches....Pages 363-414
Back Matter....Pages 415-457

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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.

Content:
Front Matter....Pages i-xxii
Introduction....Pages 1-13
Front Matter....Pages 15-15
The Future of Experimental Research....Pages 17-49
Design and Analysis of Computational Experiments: Overview....Pages 51-72
The Generation of Experimental Data for Computational Testing in Optimization....Pages 73-101
The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison....Pages 103-130
Algorithm Engineering: Concepts and Practice....Pages 131-158
Front Matter....Pages 159-159
Algorithm Survival Analysis....Pages 161-184
On Applications of Extreme Value Theory in Optimization....Pages 185-207
Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization....Pages 209-222
Front Matter....Pages 223-223
Mixed Models for the Analysis of Optimization Algorithms....Pages 225-264
Tuning an Algorithm Using Design of Experiments....Pages 265-286
Using Entropy for Parameter Analysis of Evolutionary Algorithms....Pages 287-310
F-Race and Iterated F-Race: An Overview....Pages 311-336
The Sequential Parameter Optimization Toolbox....Pages 337-362
Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches....Pages 363-414
Back Matter....Pages 415-457
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