標(biāo)題: Titlebook: Experimental Methods for the Analysis of Optimization Algorithms; Thomas Bartz-Beielstein,Marco Chiarandini,Mike Pre Book 2010 Springer-Ve [打印本頁(yè)] 作者: 里程表 時(shí)間: 2025-3-21 19:56
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書(shū)目名稱(chēng)Experimental Methods for the Analysis of Optimization Algorithms讀者反饋
書(shū)目名稱(chēng)Experimental Methods for the Analysis of Optimization Algorithms讀者反饋學(xué)科排名
作者: FLASK 時(shí)間: 2025-3-21 21:10 作者: 清澈 時(shí)間: 2025-3-22 01:05 作者: MURKY 時(shí)間: 2025-3-22 04:48 作者: agitate 時(shí)間: 2025-3-22 11:51 作者: Licentious 時(shí)間: 2025-3-22 13:37 作者: Licentious 時(shí)間: 2025-3-22 20:05 作者: 知道 時(shí)間: 2025-3-22 22:59 作者: ACME 時(shí)間: 2025-3-23 04:55
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作者: Genistein 時(shí)間: 2025-3-23 08:16
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作者: 認(rèn)為 時(shí)間: 2025-3-23 10:16 作者: Charitable 時(shí)間: 2025-3-23 15:53 作者: champaign 時(shí)間: 2025-3-23 21:53
The Sequential Parameter Optimization Toolboxactical and theoretical optimization problems. We describe the mechanics and interfaces employed by SPOT to enable users to plug in their own algorithms. Furthermore, two case studies are presented to demonstrate how SPOT can be applied in practice, followed by a discussion of alternative metamodels作者: indicate 時(shí)間: 2025-3-24 00:05
Sequential Model-Based Parameter Optimization: an Experimental Investigation of Automated and Interaild a response surface model and use this model for finding good parameter settings of the given algorithm. We evaluated two methods from the literature that are based on Gaussian process models: sequential parameter optimization (SPO) (Bartz-Beielstein et al. 2005) and sequential Kriging optimizati作者: 神秘 時(shí)間: 2025-3-24 05:39
David R. Barraclough,Angelo De Santisysis techniques, which allow us to reduce computation time, censoring the runtimes of the slower algorithms. Here, we review the statistical aspects of our online selection method, discussing the bias induced in the runtime distributions (RTD) models by the competition of different algorithms on the same problem instances.作者: Infant 時(shí)間: 2025-3-24 08:17
https://doi.org/10.1007/978-94-007-0403-9 and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice.作者: 祖先 時(shí)間: 2025-3-24 12:50
Yu Li,Jonathan Li,Michael A. Chapmantechnique and discuss an extension of the initial . algorithm, which leads to a family of algorithms that we call iterated .. Experimental results comparing one specific implementation of iterated . to the original . algorithm confirm the potential of this family of algorithms.作者: convulsion 時(shí)間: 2025-3-24 17:50
Algorithm Survival Analysisysis techniques, which allow us to reduce computation time, censoring the runtimes of the slower algorithms. Here, we review the statistical aspects of our online selection method, discussing the bias induced in the runtime distributions (RTD) models by the competition of different algorithms on the same problem instances.作者: 潰爛 時(shí)間: 2025-3-24 21:10 作者: 元音 時(shí)間: 2025-3-25 02:03 作者: Encoding 時(shí)間: 2025-3-25 05:25 作者: extemporaneous 時(shí)間: 2025-3-25 07:41 作者: 期滿(mǎn) 時(shí)間: 2025-3-25 12:26
https://doi.org/10.1007/978-3-319-06874-9le experimental designs. The example is a component-wise analysis of local search algorithms for the 2-edge-connectivity augmentation problem. We use standard statistical software to perform the analysis and report the R commands. Data sets and the analysis in SAS are available in an online compendium.作者: 整理 時(shí)間: 2025-3-25 17:57
On Applications of Extreme Value Theory in Optimizationd the performances of the procedures investigated, analytically and by simulations. In particular, we find that the estimated extreme value distributions and the fit to the outcomes characterize the performance of the optimizer in one single instance.作者: PLAYS 時(shí)間: 2025-3-25 22:28
Mixed Models for the Analysis of Optimization Algorithmsle experimental designs. The example is a component-wise analysis of local search algorithms for the 2-edge-connectivity augmentation problem. We use standard statistical software to perform the analysis and report the R commands. Data sets and the analysis in SAS are available in an online compendium.作者: Accede 時(shí)間: 2025-3-26 03:59 作者: misshapen 時(shí)間: 2025-3-26 05:50 作者: arthrodesis 時(shí)間: 2025-3-26 11:05
Geomagnetic Diagnosis of the Magnetosphereen lead to a full distributional characterization. This approach to the experimental assessment and comparison of MO performance is based on statistical inference methodology, in particular, estimation and hypothesis testing.作者: 附錄 時(shí)間: 2025-3-26 13:10 作者: DAMP 時(shí)間: 2025-3-26 20:36
The Future of Experimental Researchn experimental perspective. Therefore, we make use of the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems.作者: 牙齒 時(shí)間: 2025-3-26 21:08 作者: 防御 時(shí)間: 2025-3-27 01:52 作者: 轉(zhuǎn)折點(diǎn) 時(shí)間: 2025-3-27 07:22 作者: STING 時(shí)間: 2025-3-27 09:42 作者: 使隔離 時(shí)間: 2025-3-27 14:13
,Bluff Group—The Cayman Formation,dology is needed. In the natural sciences, this methodology relies on the mathematical framework of statistics. This book collects the results of recent research that focused on the application of statistical principles to the specific task of analyzing optimization algorithms.作者: linguistics 時(shí)間: 2025-3-27 21:43 作者: allude 時(shí)間: 2025-3-27 23:00
Introduction,dology is needed. In the natural sciences, this methodology relies on the mathematical framework of statistics. This book collects the results of recent research that focused on the application of statistical principles to the specific task of analyzing optimization algorithms.作者: 下邊深陷 時(shí)間: 2025-3-28 06:10 作者: Abjure 時(shí)間: 2025-3-28 07:36
M. Paegelow,M. T. Camacho Olmedo,J. F. MasThis chapter is a tutorial on using a . approach for tuning the parameters that affect algorithm performance. A case study illustrates the application of the method and interpretation of its results.作者: Mangle 時(shí)間: 2025-3-28 10:34
Tuning an Algorithm Using Design of ExperimentsThis chapter is a tutorial on using a . approach for tuning the parameters that affect algorithm performance. A case study illustrates the application of the method and interpretation of its results.作者: 忙碌 時(shí)間: 2025-3-28 18:40
,Bluff Group—The Cayman Formation,ithout need to follow or test a theory. Yet, in order to make conclusions based on experiments trustworthy, reliable, and objective a systematic methodology is needed. In the natural sciences, this methodology relies on the mathematical framework of statistics. This book collects the results of rece作者: 阻礙 時(shí)間: 2025-3-28 19:33 作者: 經(jīng)典 時(shí)間: 2025-3-29 01:38 作者: Innovative 時(shí)間: 2025-3-29 06:05
https://doi.org/10.1007/978-3-031-30574-0ability, high cost or inconvenience of the collection of real-world data motivates the generation of synthetic data. In many experiments, the method chosen to generate synthetic data can significantly affect the results of an experiment. Unfortunately, the scientific literature does not contain gene作者: Toxoid-Vaccines 時(shí)間: 2025-3-29 07:48
Geomagnetic Diagnosis of the Magnetosphere the random outcomes of stochastic MOs, such as multiobjective evolutionary algorithms, are sets of nondominated solutions, analyzing their performance is challenging in that it involves studying the distribution of those sets. The attainment function, so named because it indicates the probability o作者: 陰郁 時(shí)間: 2025-3-29 11:53
Records of Paleomagnetic Field Variations, more. We discuss the major weaknesses of traditional “pen and paper” algorithmics and the ever-growing gap between theory and practice in the context of modern computer hardware and real-world problem instances. We present the key ideas and concepts of the central . cycle that is based on a full fe作者: FRAX-tool 時(shí)間: 2025-3-29 18:02
David R. Barraclough,Angelo De Santispensive. In recent work, we adopted an . approach, in which models of the runtime distributions of the available algorithms are iteratively updated and used to guide the allocation of computational resources, while solving a sequence of problem instances. The models are estimated using survival anal作者: 享樂(lè)主義者 時(shí)間: 2025-3-29 22:28
Geomagnetic Observatory and Survey Practicenuous objective functions. We discuss the application of extreme value theory for the optimization procedures. A short review of the extreme value theory is presented to understand the investigations. In this chapter three optimization procedures are compared in this context: the random search and t作者: Accord 時(shí)間: 2025-3-30 00:17 作者: commune 時(shí)間: 2025-3-30 07:16 作者: 平息 時(shí)間: 2025-3-30 11:11 作者: Femish 時(shí)間: 2025-3-30 13:38
Yu Li,Jonathan Li,Michael A. Chapmanance is optimized. In this chapter, we review ., a racing algorithm for the task of automatic algorithm configuration. . is based on a statistical approach for selecting the best configuration out of a set of candidate configurations under stochastic evaluations. We review the ideas underlying this 作者: eczema 時(shí)間: 2025-3-30 19:03
https://doi.org/10.1007/978-3-031-34765-8actical and theoretical optimization problems. We describe the mechanics and interfaces employed by SPOT to enable users to plug in their own algorithms. Furthermore, two case studies are presented to demonstrate how SPOT can be applied in practice, followed by a discussion of alternative metamodels作者: 昆蟲(chóng) 時(shí)間: 2025-3-30 21:42
Florian W. H. Smit,Michael John Welchild a response surface model and use this model for finding good parameter settings of the given algorithm. We evaluated two methods from the literature that are based on Gaussian process models: sequential parameter optimization (SPO) (Bartz-Beielstein et al. 2005) and sequential Kriging optimizati
