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comparison of algorithms for reconstructing hv-convex discrete sets

LINEAR ALGEBRA AND ITS APPLICATIONS 2001年第1-3期339卷 23-35页

作者： Balogh, E Kuba, A Dévényi, C Del Lungo, A Univ Szeged Dept Informat H-6720 Szeged Hungary Univ Siena Dipartimento Matemat I-53100 Siena Italy

Three reconstruction algorithms to be used for reconstructing hv-convex discrete sets from their row and column sums are compared. All these algorithms have two versions. one for reconstructing hv-convex polyominoes and another one for reconstructing hv-convex 8-connected discrete sets. In both classes of discrete sets the algorithms are compared from the viewpoints of average execution time and memory complexities. Discrete sets with given sizes are generated with uniform distribution, and then reconstructed from their row and column sums. First we have implemented two previously published algorithms. According to our comparisons, the algorithm which was better from the viewpoint of worst time complexity was the worse from the viewpoint of average time complexity and memory requirements. Then, as a new method, a combination of the two algorithms was also implemented and it is shown that it inherits the best properties of the other two methods. (C) 2001 Elsevier Science Inc. All rights reserved.

关键词： discrete tomography reconstruction from projections comparison of algorithms

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Sensitivity analysis and comparison of algorithms in preventive maintenance and replacement scheduling optimization models

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COMPUTERS & INDUSTRIAL ENGINEERING 2011年第1期61卷 64-75页

作者： Moghaddam, Kamran S. Usher, John S. Univ Louisville Dept Ind Engn Louisville KY 40292 USA

In this research, new optimization models are developed to determine the optimal preventive maintenance and replacement schedules in repairable and maintainable systems. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability over the planning horizon. Experimental results of a sensitivity analysis on the optimization models are presented and evaluated. These experiments investigate the effect of the parameters on the structure of optimal preventive maintenance and replacement schedules in multi-component systems. Two factorial design experiments based on the cost associated with maintenance and replacement activities and reliability characteristic parameters are constructed and analyzed. In addition, a comprehensive experiment is designed to analyze and compare the efficiency and accuracy of the exact and metaheuristic algorithms. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Preventive maintenance and replacement scheduling Optimization models Multi-component systems Sensitivity analysis comparison of algorithms

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Advances in Assignment Problem and comparison of algorithms

Advances in Assignment Problem and Comparison of Algorithms

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27th Chinese Control Conference

作者： Yan Chaobo Zhao Qianchuan Tsinghua Univ Dept Automat Beijing 100080 Peoples R China

ISBN: (纸本)9787900719706

Assignment Problem (AP) was well studied in the past 50 years, and is of great value in operations research and engineering. The Hungarian Method is one of the effective algorithms for the assignment problem. Although the assignment problem is well studied and a variety of algorithms are proposed, the efficiencies of the algorithms are never completely compared in the literature. In this paper, we summarize the properties of the assignment problem, and then survey its research history, add two new algorithms to existing classification, and compare the performances of these two algorithms with that of the Hungarian Method. The computational results show that the Hungarian Method can solve larger balanced assignment problems in engineering and is more efficient than the two algorithms involved, and the bidding algorithm is superior to solve unbalanced assignment problems.

关键词： Assignment problem The hungarian method comparison of algorithms

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comparison of Evolutionary algorithms for Estimation of Parameters of the Equivalent Circuit of an AC Motor 1

Comparison of Evolutionary Algorithms for Estimation of Para...

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1st IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI)

作者： Ramos, Guillermo A. Lopez, Jesus A. Serv Nacl Aprendizaje SENA Cali Colombia Univ Autonoma Occidente Dept Automat & Elect Cali Colombia

ISBN: (纸本)9781538667408

This work shows the comparison among three evolutionary algorithms used to estimate the parameters of the equivalent circuit of a three-phase induction motor. With the parameters of the motor is possible to calculate its efficiency. Applying statistical methods, the number of runs needed to obtain a confidence level of 95% is calculated. With this value each algorithm is used to estimate the motor's parameters and, according to the results, is possible to find the best

关键词： Evolutionary algorithms Estimation of Parameters AC motor comparison of algorithms

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comparison of evolutionary algorithms implemented in different programming languages

ELEKTROTEHNISKI VESTNIK

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ELEKTROTEHNISKI VESTNIK 2022年第1-2期89卷 46-52页

作者： Ravber, Miha Moravec, Matej Mernik, Marjan Univ Maribor Fak Elektrotehn Racunalnistvo Informat Koroska Cesta 46 Maribor 2000 Slovenia

The performance of the evolutionary algorithms is assessed by comparing them with the existing algorithms. comparison of the evolutionary algorithms in the literature plays a key role as it illustrates which algorithms are better. It is a common practice to simply take the results from the papers of the algorithms you are comparing your proposed algorithm to. However, it is not guaranteed that the experiments are performed in accordance with the guidelines for a fair comparison. Since a correct execution of an experiment is crucial to be able to state with a certainty the superiority of evolutionary algorithms, it is recommended that the experiment is repeated. This, however, can be a challenge if the source code of the algorithm is not available or it needs to be implemented in another programming language. Such approach can lead to misleading results due to the unintentional mistakes made in implementing the algorithm. Another factor that is often overlooked are the differences between the programming languages in which the algorithm is implemented. The problem is highlighted by analyzing and comparing the performance of the GWO algorithm implemented in six programming languages.

关键词： evolutionary algorithms grey wolf optimizer comparison of algorithms algorithms performance

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comparison OF GENERALIZED GEOMETRIC-PROGRAMMING algorithms

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 1978年第2期26卷 205-242页

作者： RIJCKAERT, MJ MARTENS, XM Professor Doctoral Student Instituut voor Chemie-Ingenieurstechniek Katholieke Universiteit te Leuven Leuvem Belgium

Numerical results are presented of extensive tests involving five posynomial and twelve signomial programming codes. The set of test problems includes problems with a pure mathematical meaning as well as problems originating from different fields of engineering. The algorithms are compared on the basis of CPU time, number of failures, preparation time, and in-core storage.

关键词： Geometric programming algorithms comparison of algorithms test problems numerical methods computing methods

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Performance comparison of Machine Learning Platforms

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INFORMS JOURNAL ON COMPUTING 2019年第2期31卷 207-225页

作者： Roy, Asim Qureshi, Shiban Pande, Kartikeya Nair, Divitha Gairola, Kartik Jain, Pooja Singh, Suraj Sharma, Kirti Jagadale, Akshay Lin, Yi-Yang Sharma, Shashank Gotety, Ramya Zhang, Yuexin Tang, Ji Mehta, Tejas Sindhanuru, Hemanth Okafor, Nonso Das, Santak Gopal, Chidambara N. Rudraraju, Srinivasa B. Kakarlapudia, Avinash, V Arizona State Univ Dept Informat Syst Tempe AZ 85287 USA

In this paper, we present a method for comparing and evaluating different collections of machine learning algorithms on the basis of a given performance measure (e.g., accuracy, area under the curve (AUC), F-score). Such a method can be used to compare standard machine learning platforms such as SAS, IBM SPSS, and Microsoft Azure ML. A recent trend in automation of machine learning is to exercise a collection of machine learning algorithms on a particular problem and then use the best performing algorithm. Thus, the proposed method can also be used to compare and evaluate different collections of algorithms for automation on a certain problem type and find the best collection. In the study reported here, we applied the method to compare six machine learning platforms - R, Python, SAS, IBM SPSS Modeler, Microsoft Azure ML, and Apache Spark ML. We compared the platforms on the basis of predictive performance on classification problems because a significant majority of the problems in machine learning are of that type. The general question that we addressed is the following: Are there platforms that are superior to others on some particular performance measure? For each platform, we used a collection of six classification algorithms from the following six families of algorithms - support vector machines, multilayer perceptrons, random forest (or variant), decision trees/ gradient boosted trees, Naive Bayes/Bayesian networks, and logistic regression. We compared their performance on the basis of classification accuracy, F-score, and AUC. We used F-score and AUC measures to compare platforms on two-class problems only. For testing the platforms, we used a mix of data sets from (1) the University of California, Irvine (UCI) library, (2) the Kaggle competition library, and (3) high-dimensional gene expression problems. We performed some hyperparameter tuning on algorithms wherever possible.

关键词： machine learning platforms classification algorithms comparison of algorithms comparison of platforms

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MACHINE LEARNING FOR OCCUPATION CODING-A comparison STUDY

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JOURNAL OF SURVEY STATISTICS AND METHODOLOGY 2021年第5期9卷 1013-1034页

作者： Schierholz, Malte Schonlau, Matthias Inst Employment Res IAB Regensburger Str 104 D-90478 Nurnberg Germany Univ Waterloo 200 Univ Ave West Waterloo ON N2L 3G1 Canada

Asking people about their occupation is common practice in surveys and censuses around the world. The answers are typically recorded in textual form and subsequently assigned (coded) to categories, which have been defined in official occupational classifications. While this coding step is often done manually, substituting it with more automated workflows has been a longstanding goal, promising reduced data-processing costs and accelerated publication of key statistics. Although numerous researchers have developed different algorithms for automated occupation coding, the algorithms have rarely been compared with each other or tested on different data sets. We fill this gap by comparing some of the most promising algorithms found in the literature and testing them on five data sets from Germany. The first two algorithms we test exemplify a common practice in which answers are coded automatically according to a predefined list of job titles. Statistical learning algorithms-that is, regularized multinomial regression, tree boosting, or algorithms developed specifically for occupation coding (algorithms three to six)-can improve upon algorithms one and two, but only if a sufficient number of training observations from previous surveys is available. The best results are obtained by merging the list of job titles with coded answers from previous surveys before using this combined training data for statistical learning (algorithm 7). However, the differences between the algorithms are often small compared to the large variation found across different data sets, which we ascribe to systematic differences in the way the data were coded in the first place. Such differences complicate the application of statistical learning, which risks perpetuating questionable coding decisions from the training data to the future.

关键词： Coding comparison of algorithms Data processing Machine learning Occupation Statistical learning

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A Particular Method of Modelling Stochastic Intensity Fields by Isotropic, One-Step Markov Chains

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TM-TECHNISCHES MESSEN 2012年第5期79卷 271-276页

作者： Dubinin, Dmitry Geringer, Viktor Kochegurov, Alexander Tomsker Staatliche Univ Syst Steuerung & Nachrich Tomsk 634050 Russia Tomsker Polytech Univ Tomsk 634050 Russia

This paper outlines a particular method of modelling stochastic intensity fields by isotropic, one-step Markov chains. The field characteristics are determined among each other by "Palma formulas" whereas the correlating characteristics of the generated random fields depend only on the mosaic grating structure. The alphabetical/ABC selection based on the grating structure (morphology of the field) is determined by the operator manually in advance. The presented method allows the generating of different types of grating structures with horizontal, vertical, and diagonal elements in an 8-adjacency.

关键词： Stochastic modelling comparison of algorithms homogeneous Markov random fields stochastic fields

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Improved online algorithms for jumbled matching

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DISCRETE APPLIED MATHEMATICS 2020年 274卷 54-66页

作者： Ghuman, Sukhpal Singh Tarhio, Jorma Chhabra, Tamanna Sheridan Coll Fac Appl Sci & Technol 7899 McLaughlin Rd Brampton ON L6Y 5H9 Canada Aalto Univ Dept Comp Sci POB 15400 FI-00076 Aalto Finland Seneca Coll 70 Pond Rd Toronto ON M3J 3M6 Canada

We consider the problem of jumbled matching where the objective is to find all permuted occurrences of a pattern in a text. Besides exact matching we study approximate matching where each occurrence is allowed to contain at most k wrong or superfluous characters. We present online algorithms applying bit-parallelism to both types of jumbled matching. Two of our algorithms are filtration methods applying SIMD (Single Instruction Multiple Data) computation. Most of the other new algorithms are variations of earlier methods. We show by practical experiments that our algorithms are competitive with previous solutions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Jumbled matching Abelian matching Approximate string matching SIMD computation comparison of algorithms Algorithm engineering

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