Name: Experimental Methods for the Analysis of Optimization Algorithms de Springer(Springer) (en Inglés)
SKU: 62113556
Price: 104.67 USD
Availability: InStock
Author: Bartz-Beielstein Thomas,Chiarandini Marco,Paquete Luís
ISBN: 9783642445903

portada Experimental Methods for the Analysis of Optimization Algorithms de Springer(Springer) (en Inglés)

Experimental Methods for the Analysis of Optimization Algorithms de Springer(Springer) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Bartz-Beielstein Thomas,Chiarandini Marco,Paquete Luís

Idioma

Inglés

N° páginas

480

Encuadernación

Tapa Blanda

ISBN13

9783642445903

Experimental Methods for the Analysis of Optimization Algorithms de Springer(Springer) (en Inglés)

Bartz-Beielstein Thomas,Chiarandini Marco,Paquete Luís (Autor) · Springer · Tapa Blanda

Libro Nuevo Importado

Envío: 16 a 23 días háb.

$ 190.30$ 104.67

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan más de 100 unidades

$ 104.67

Llega entre el 29 Sep y el 13 Oct a Quito, Pichincha. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Experimental Methods for the Analysis of Optimization Algorithms de Springer(Springer) (en Inglés)"

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.