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Titlebook: Experimental Methods for the Analysis of Optimization Algorithms; Thomas Bartz-Beielstein,Marco Chiarandini,Mike Pre Book 2010 Springer-Ve

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書目名稱Experimental Methods for the Analysis of Optimization Algorithms
編輯Thomas Bartz-Beielstein,Marco Chiarandini,Mike Pre
視頻videohttp://file.papertrans.cn/319/318874/318874.mp4
概述First book to offer full treatment on this subject.Contributor include leading scientists in algorithm design, statistical design, optimization and heuristics.Most chapters provide theoretical backgro
圖書封面Titlebook: Experimental Methods for the Analysis of Optimization Algorithms;  Thomas Bartz-Beielstein,Marco Chiarandini,Mike Pre Book 2010 Springer-Ve
描述.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 scien
出版日期Book 2010
關(guān)鍵詞Algorithm engineering; Algorithms; Combinatorial optimization; Evolutionary algorithms; Experiment desig
版次1
doihttps://doi.org/10.1007/978-3-642-02538-9
isbn_softcover978-3-642-44590-3
isbn_ebook978-3-642-02538-9
copyrightSpringer-Verlag Berlin Heidelberg 2010
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Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimizationective optimization problems. These tools are based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. In particular, we consider the visualization of attainment surfaces
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Mixed Models for the Analysis of Optimization Algorithmsork to separate the effects of algorithmic components and instance features included in the analysis. We regard test instances as drawn from a population and we focus our interest not on those single instances but on the whole population. Hence, instances are treated as a .. Overall these experiment
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