Experimental Methods for the Analysis of Optimization Algorithms

ISBN-13: 9783642445903 / Angielski / Miękka / 2014 / 457 str.

Thomas Bartz-Beielstein; Marco Chiarandini; Luis Paquete

Opis

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.

Recenzje

"This book belongs on the shelf of anyone interested in carrying out experimental research on algorithms and heuristics for optimization problems. ... Don't keep this book on the shelf: read it, and apply the techniques and tools contained herein to your own algorithmic research project. Your experiments will become more efficient and more trustworthy, and your experimental data will lead to clearer and deeper insights about performance." (Catherine C. McGeoch, Amherst College)

"Here you will find aspects that are treated scientifically by the experts in this exciting domain offering their up-to-date know-how and even leading into philosophical domains." (Hans-Paul Schwefel, Technische Universität Dortmund)

"[This] book ... is a solid and comprehensive step forward in the right direction. [It] not only covers adequate comparison of methodologies but also the tools aimed at helping in algorithm design and understanding, something that is being recently referred to as 'Algorithm Engineering'. [It] is of interest to two distinct audiences. First and foremost, it is targeted at the whole operations research and management science, artificial intelligence and computer science communities with a loud and clear cry for attention. Strong, sound and reliable tools should be employed for the comparison and assessment of algorithms and also for more structured algorithm engineering. Given the level of detail of some other chapters however, a second potential audience could be made up of those researchers interested in the core topic of algorithm assessment. The long list of contributors to this book includes top notch and experienced researchers that, together, set the trend in the field. As a result, those interested in this specific area of analysis of optimization algorithms should not miss this book under any circumstance. ... The careful, sound, detailed and comprehensive assessment of optimization algorithms is a necessity that requires attention and care. As a result, my opinion is that this book should be followed and that it should be at the top of every experimenter's table." (Rubén Ruiz, European Journal of Operational Research, 2011, 214(2):453-456)

Spis treści

Overview.- The Future of Experimental Research.- Design and Analysis of Computational Experiments: Overview.- The Generation of Experimental Data for Computational Testing in Optimization.- The Attainment-Function Approach to Stochastic Multiobjective Optimizer Assessment and Comparison.- Algorithm Engineering: Concepts and Practice.- Characterizing Algorithm Performance.- Algorithm Survival Analysis.- On Applications of Extreme Value Theory in Optimization.- Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization.- Algorithm Configuration and Tuning.- Mixed Models for the Analysis of Optimization Algorithms.- Tuning an Algorithm Using Design of Experiments.- Using Entropy for Parameter Analysis of Evolutionary Algorithms.- F-Race and Iterated F-Race: An Overview.- The Sequential Parameter Optimization Toolbox.- Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interactive Approaches.