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The ELAPS framework: Experimental Linear Algebra Performance Studies

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2019年第2期33卷 353-365页

作者： Peise, Elmar Bientinesi, Paolo Rhein Westfal TH Aachen AICES Schinkelstr 2 D-52062 Aachen Germany

In scientific computing, optimal use of computing resources comes at the cost of extensive coding, tuning, and benchmarking. While the classic approach of "features first, performance later" is supported by a variety of tools such as Tau, Vampir, and Scalasca, the emerging performance-centric approach, in which both features and performance are primary objectives, is still lacking suitable development tools. For dense linear algebra applications, we fill this gap with the Experimental Linear Algebra Performance Studies (ELAPS) framework, a multi-platform open-source environment for easy, fast, and yet powerful performance experimentation and prototyping. In contrast to many existing tools, ELAPS targets the beginning of the development process, assisting application developers in both algorithmic and optimization decisions. With ELAPS, users construct experiments to investigate how performance and efficiency depend on factors such as caching, algorithmic parameters, problem size, and parallelism. Experiments are designed either through Python scripts or a specialized Graphical User Interface (GUI), and run on a spectrum of architectures, ranging from laptops to accelerators and clusters. The resulting reports provide various metrics and statistics that can be analyzed both numerically and visually. In this article, we introduce ELAPS and illustrate its practical value in guiding critical performance decisions already in early development stages.

关键词： Performance dense linear algebra experimentation algorithm selection performance prediction

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A sequential algorithm portfolio approach for black box optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 44卷 559-570页

作者： He, Yaodong Yuen, Shiu Yin Lou, Yang Zhang, Xin City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China

A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach.

关键词： algorithm portfolio algorithm selection Heuristic algorithms Optimization problems Performance prediction

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Adaptive recommendation model using meta-learning for population-based algorithms

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INFORMATION SCIENCES 2019年 476卷 192-210页

作者： Chu, Xianghua Cai, Fulin Cui, Can Hu, Mengqi Li, Li Qin, Quande Shenzhen Univ Coll Management Shenzhen Peoples R China Shenzhen Univ Inst Big Data Intelligent Management & Decis Shenzhen Peoples R China Arizona State Univ Sch Comp Informat Decis Syst Engn Tempe AZ 85287 USA Univ Illinois Dept Mech & Ind Engn Chicago IL USA

To efficiently solve complex optimization problems, numerous population-based meta heuristics and extensions have been developed. However, the performances of the algorithms vary depending on the problems. In this research, we propose an Adaptive Recommendation Model (ARM) using meta-learning to identify appropriate problem-dependent population-based algorithm. In ARM, the algorithms are adaptively selected by mapping the problem characteristics to the algorithm performance. Since the meta-features extracted and meta-learner adopted would significantly affect the system performance, 18 meta-features including statistical, geometrical and landscape features are extracted to characterize optimization problem spaces. Both instance-based and model-based learners are investigated. Two performance metrics, Spearman's rank correlation coefficient and success rate are used to evaluate the accuracy of optimizer's ranking prediction and the precision of the best optimizer recommendation. The proposed ARM is compared against population-based algorithms with distinct search capabilities such as PSO variants, non-PSO population-based optimizers, hyper-heuristics and ensemble methods. Benchmark functions and real-world problems with various properties are adopted in the experiments. Experimental results reveal the extendibility and effectiveness of ARM on the diverse tested problems in terms of solution accuracy, ranking and success rate. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Global optimization algorithm selection Meta-learning Population-based algorithm Recommendation system

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A Cloud-based Network of 3D Objects for Robust Grasp Planning 20

A Cloud-based Network of 3D Objects for Robust Grasp Plannin...

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Proceedings of the 2020 1st International Conference on Control, Robotics and Intelligent System

作者： Sergey Muravyov Andrey Filchenkov ITMO University Russia Federation

ISBN: (纸本)9781450388054

Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We propose a new GG-CNN architecture for DexNet, provide a new way for dataset generation for the GG-CNN and describe practical improvements that increase the model validation accuracy and other performance aspects of the whole system

关键词： Dataset representation Generative adversarial nets algorithm selection Dataset synthesis Meta-learning Machine learning Graph convolutional network

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Selecting evolutionary algorithms for black box design optimization problems

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SOFT COMPUTING 2019年第15期23卷 6511-6531页

作者： Yuen, Shiu Yin Lou, Yang Zhang, Xin City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Tianjin Normal Univ Coll Elect & Commun Engn Tianjin Peoples R China

An algorithm selection method for black box design optimization problems is reported. It uses a simple and natural principle to select an algorithm set from a set of algorithm candidates. A set of benchmark problems is given, and the performance of each algorithm in the set is recorded in a knowledge base. Given an unknown problem, the default algorithm is run. An algorithm-problem feature is proposed and used to map to the most similar benchmark problem. Then the best algorithm for solving the problem is used in the second run. This process iterates until n runs have been made. The best result out of n runs is returned as the solution. Experimental results reveal that the algorithm-problem feature is a good problem identifier. Results are also reported when (1) both the training and testing set are the set of benchmark problems;and (2) the training set is the set of benchmark problems but the testing set is a set of real-world benchmark problems. The method works well on both scenarios. It attains almost the same performance as the best algorithm, and has better performance compared with random selection. As the best algorithm cannot be known a priori, the results confirm that the algorithm selection mechanism is effective. The performance of the algorithm as a function of n and the case when n is equal to two is also studied.

关键词： Evolutionary algorithm algorithm selection Benchmarking performance Design optimization problems Black box problems

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A Novel Framework for Recommending Data Mining algorithm in Dynamic IoT Environment

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IEEE ACCESS 2020年 8卷 157333-157345页

作者： Hossain, M. Anwar Ferdousi, Rahatara Hossain, Sk Alamgir Alhamid, Mohammed F. Saddik, Abdulmotaleb El King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia Adv Syst & Software Lab ASySLab Khulna 9100 Bangladesh Khulna Univ Comp Sci & Engn Discipline Khulna 9208 Bangladesh Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada

Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper proposes a knowledge-driven framework that considers the knowledge of datasets, available DM algorithms, and application goals to select the suitable DM algorithm for performing a target data processing task. This work considers data from cultural domain, health domain, and transportation domain in the experiment. The results show that the proposed approach dynamically selects the best-suited DM algorithms for the available datasets and target goals that exhibits satisfactory performance in obtaining accurate results compared to the existing work. The proposed approach not only provides flexibility in conducting dynamic IoT data mining tasks, but also reduces the complexity that would otherwise be necessary while adopting the traditional data mining approaches.

关键词： Heuristic algorithms Data mining Machine learning algorithms Machine learning Smart cities Classification algorithms Internet of Things Internet of Things data mining machine learning algorithm selection

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AutoGRD: Model Recommendation Through Graphical Dataset Representation 19

AutoGRD: Model Recommendation Through Graphical Dataset Repr...

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28th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Cohen-Shapira, Noy Rokach, Lior Shapira, Bracha Katz, Gilad Vainshtein, Roman Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781450369763

The widespread use of machine learning algorithms and the high level of expertise required to utilize them have fuelled the demand for solutions that can be used by non-experts. One of the main challenges non-experts face in applying machine learning to new problems is algorithm selection - the identification of the algorithm(s) that will deliver top performance for a given dataset, task, and evaluation measure. We present AutoGRD, a novel meta-learning approach for algorithm recommendation. AutoGRD first represents datasets as graphs and then extracts their latent representation that is used to train a ranking meta-model capable of accurately recommending top-performing algorithms for previously unseen datasets. We evaluate our approach on 250 datasets and demonstrate its effectiveness both for classification and regression tasks. AutoGRD outperforms state-of-the-art meta-learning and Bayesian methods.

关键词： Meta-learning algorithm selection AutoML Dataset representation Classification Regression Graph embedding

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Multi-objective Performance Measurement: Alternatives to PAR10 and Expected Running Time 12th

Multi-objective Performance Measurement: Alternatives to PAR...

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12th International Conference on Learning and Intelligent Optimization (LION)

作者： Bossek, Jakob Trautmann, Heike Univ Munster Informat Syst & Stat Munster Germany

ISBN: (纸本)9783030053482;9783030053475

A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques.

关键词： algorithm selection Performance measurement

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A Multi-layer Ontology for Data Processing Techniques 16

A Multi-layer Ontology for Data Processing Techniques

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16th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Man Tianxing Zhukova, Nataly Nguyen Than Nechaev, Alexander Lebedev, Sergey ITMO Univ St Petersburg Russia Russian Acad Sci St Petersburg Inst Informat & Automat St Petersburg Russia St Petersburg Electrotech Univ St Petersburg Russia Vyatka State Univ Kirov Russia

ISBN: (纸本)9789897583803

Currently, data processing technology is applied in various fields. But non-expert researchers are always confused about its diversity and complex processes. Especially due to the instability of real data, the preparation process for extracting information is lengthy. At the same time, different analysis algorithms are based on different mathematical models, so they are suitable for different situations. In the real data processing process, inappropriate data forms and algorithm selections always lead to unsatisfactory results. This paper proposes a multilayer description model of data processing algorithms and implements it based on ontology technology. The model provides a multi-layered structure including data pre-processing, data form conversion, and output model selection so that the user can obtain a complete data processing process from it. The extensibility and interpretability of ontology also provide a huge space for model improvement. The multi-level structure greatly reduces its complexity.

关键词： Data Processing Machine Learning Multilayer Structure algorithm selection Ontology

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Rigorous Performance Analysis of State-of-the-Art TSP Heuristic Solvers 1

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19th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： McMenemy, Paul Veerapen, Nadarajen Adair, Jason Ochoa, Gabriela Univ Stirling Comp Sci & Math Stirling Scotland Univ Lille CNRS Cent Lille UMR 9189CRIStAL F-59000 Lille France

ISBN: (数字)9783030167110

ISBN: (纸本)9783030167103;9783030167110

Understanding why some problems are better solved by one algorithm rather than another is still an open problem, and the symmetric Travelling Salesperson Problem (TSP) is no exception. We apply three state-of-the-art heuristic solvers to a large set of TSP instances of varying structure and size, identifying which heuristics solve specific instances to optimality faster than others. The first two solvers considered are variants of the multi-trial Helsgaun's Lin-Kernighan Heuristic (a form of iterated local search), with each utilising a different form of Partition Crossover;the third solver is a genetic algorithm (GA) using Edge Assembly Crossover. Our results show that the GA with Edge Assembly Crossover is the best solver, shown to significantly outperform the other algorithms in 73% of the instances analysed. A comprehensive set of features for all instances is also extracted, and decision trees are used to identify main features which could best inform algorithm selection. The most prominent features identified a high proportion of instances where the GA with Edge Assembly Crossover performed significantly better when solving to optimality.

关键词： TSP algorithm selection EAX GPX Performance analysis

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