000 04432nam a22005415i 4500
001 978-3-642-02538-9
003 DE-He213
005 20140220084523.0
007 cr nn 008mamaa
008 101109s2010 gw | s |||| 0|eng d
020 _a9783642025389
_9978-3-642-02538-9
024 7 _a10.1007/978-3-642-02538-9
_2doi
050 4 _aQA276-280
072 7 _aUYAM
_2bicssc
072 7 _aUFM
_2bicssc
072 7 _aCOM077000
_2bisacsh
082 0 4 _a005.55
_223
100 1 _aBartz-Beielstein, Thomas.
_eeditor.
245 1 0 _aExperimental Methods for the Analysis of Optimization Algorithms
_h[electronic resource] /
_cedited by Thomas Bartz-Beielstein, Marco Chiarandini, Luís Paquete, Mike Preuss.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aXXII, 457p. 93 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aOverview -- 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.
520 _aIn 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.
650 0 _aComputer science.
650 0 _aAlgorithms.
650 0 _aOperations research.
650 0 _aPhysics.
650 0 _aEngineering.
650 1 4 _aComputer Science.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aOperations Research, Mathematical Programming.
650 2 4 _aAlgorithms.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aComplexity.
700 1 _aChiarandini, Marco.
_eeditor.
700 1 _aPaquete, Luís.
_eeditor.
700 1 _aPreuss, Mike.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642025372
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-02538-9
912 _aZDB-2-SCS
999 _c111419
_d111419