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Experimental Methods for the Analysis of Optimization Algorithms
eBook
ISBN/EAN: 9783642025389
Umbreit-Nr.: 1699203
Sprache:
Englisch
Umfang: 458 S., 4.66 MB
Format in cm:
Einband:
Keine Angabe
Erschienen am 02.11.2010
Auflage: 1/2010
E-Book
Format: PDF
DRM: Digitales Wasserzeichen
- Zusatztext
- <p>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.</p><p>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.</p><p>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.</p><p>
- Kurztext
- <p>With contributions from leaders in the field, this volume assesses the main issues in the experimental analysis of algorithms, examines their developmental cycle, and demonstrates how to configure and tune algorithms with advanced experimental techniques.</p>