The Resource Experimental Methods for the Analysis of Optimization Algorithms, edited by Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss, (electronic resource)

Experimental Methods for the Analysis of Optimization Algorithms, edited by Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss, (electronic resource)

Label
Experimental Methods for the Analysis of Optimization Algorithms
Title
Experimental Methods for the Analysis of Optimization Algorithms
Statement of responsibility
edited by Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss
Creator
Contributor
Editor
Provider
Subject
Language
eng
Summary
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
Member of
http://library.link/vocab/creatorName
Bartz-Beielstein, Thomas
Image bit depth
0
LC call number
QA276-280
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
  • Chiarandini, Marco.
  • Paquete, Luís.
  • Preuss, Mike
  • SpringerLink
http://library.link/vocab/subjectName
  • Computer science
  • Algorithms
  • Operations research
  • Physics
  • Engineering
  • Computer Science
  • Probability and Statistics in Computer Science
  • Operations Research, Mathematical Programming
  • Algorithms
  • Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  • Complexity
Label
Experimental Methods for the Analysis of Optimization Algorithms, edited by Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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
Dimensions
unknown
Extent
XXII, 457p. 93 illus.
File format
multiple file formats
Form of item
electronic
Isbn
9783642025389
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-642-02538-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-642-02538-9
Label
Experimental Methods for the Analysis of Optimization Algorithms, edited by Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss, (electronic resource)
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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
Dimensions
unknown
Extent
XXII, 457p. 93 illus.
File format
multiple file formats
Form of item
electronic
Isbn
9783642025389
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-642-02538-9
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-642-02538-9

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