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作者： Ochoa, Cosme University of Michigan

A diverse suite of manned and unmanned aircraft will occupy future urban airspace. Flight plans must accommodate specific aircraft characteristics, including physical volume with safety zone clearance, landing/takeoff procedures, kinodynamics, and a wide range of flight environments. No single motion planner is applicable across all possible aircraft configurations and operating conditions. This dissertation proposes the first motion planning algorithm selection capability with application to small Unmanned Aircraft System (UAS) multicopters operating in and over a complex urban landscape. Alternative data-driven fail-safe protocols are presented to improve on contemporary ``fly-home’’ or automatic landing protocols, focusing on rooftops as safe urban landing sites. In a fail-safe direct strategy, the multicopter identifies, generates, and follows a flight plan to the closest available rooftop suitable for landing. In a fail-safe supervisory strategy, the multicopter examines rooftops en route to a planned landing site, diverting to a closer, clear landing site when possible. In a fail-safe coverage strategy, the multicopter cannot preplan a safe landing site due to missing data. The multicopter executes a coverage path to explore the area and evaluate overflown rooftops to find a safe landing site. These three fail-safe algorithms integrate map generation, flight planning, and area coverage capabilities. The motion planning algorithm selection problem (ASP) requires qualitative and quantitative metrics to inform the ASP of user/agent, algorithm, and configuration space preferences and constraints. Urban flight map-based, path-based, and software-based cost metrics are defined to provide insights into the urban canyon properties needed to construct safe and efficient flight plans. Map-based metrics describe the operating environment by constructing a collection of GPS/Lidar navigation performance, population density, and obstacle risk exposure metric maps. Path-ba

关键词： algorithm selection fail-safe protocols urban flight metrics motion planning data science machine learning Thesis

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To quantum or not to quantum: towards algorithm selection in near-term quantum optimization

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QUANTUM SCIENCE AND TECHNOLOGY 2020年第4期5卷

作者： Moussa, Charles Calandra, Henri Dunjko, Vedran Leiden Univ LIACS Niels Bohrweg 1 NL-2333 CA Leiden Netherlands TOTAL SA Courbevoie France

The Quantum approximate optimization algorithm (QAOA) constitutes one of the often mentioned candidates expected to yield a quantum boost in the era of near-term quantum computing. In practice, quantum optimization will have to compete with cheaper classical heuristic methods, which have the advantage of decades of empirical domain-specific enhancements. Consequently, to achieve optimal performance we will face the issue of algorithm selection, well-studied in practical computing. Here we introduce this problem to the quantum optimization domain. Specifically, we study the problem of detecting those problem instances of where QAOA is most likely to yield an advantage over a conventional algorithm. As our case study, we compare QAOA against the well-understood approximation algorithm of Goemans and Williamson on the Max-Cut problem. As exactly predicting the performance of algorithms can be intractable, we utilize machine learning (ML) to identify when to resort to the quantum algorithm. We achieve cross-validated accuracy well over 96%, which would yield a substantial practical advantage. In the process, we highlight a number of features of instances rendering them better suited for QAOA. While we work with simulated idealised algorithms, the flexibility of ML methods we employed provides confidence that our methods will be equally applicable to broader classes of classical heuristics, and to QAOA running on real-world noisy devices.

关键词： algorithm selection quantum algorithms combinatorial optimization

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ImageDataset2Vec: An image dataset embedding for algorithm selection

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 180卷 115053-115053页

作者： Dias, Lucas V. Miranda, Pericles B. C. Nascimento, Andre C. A. Cordeiro, Filipe R. Mello, Rafael Ferreira Prudencio, Ricardo B. C. Univ Fed Rural Pernambuco Dept Comp Recife PE Brazil Univ Fed Pernambuco Ctr Informat Recife PE Brazil

Convolutional Neural Networks (CNNs) have become the main solution for image classification tasks in different applications. Although several CNN architectures are available, there is no best architecture regardless the problem at hand. The selection of the most suitable CNN architecture is usually performed by trial and error, which may take much time and computational resources. Meta-learning (MtL) is a framework developed in machine learning to perform algorithm selection based on the meta-features of each task being solved. Such meta-features are usually descriptive characteristics extracted from the training dataset available in the task at hand. Despite the increasing attention of MtL for algorithm selection, its success strongly depends on defining relevant meta-features to represent the classification tasks of interest. This paper proposes the ImageDataset2Vec method for extracting meta-features to describe image classification datasets. ImageDataset2Vec adopts a pretrained deep neural network to extract features from images datasets, embedding them in a single feature vector. The derived meta-features are employed by MtL to select CNN architectures for image classification. The proposed approach was evaluated for selecting among six CNN algorithms in 45 two-classes image datasets. The results showed that MtL using ImageDataset2Vec overcame different baseline methods, selecting the best possible CNN algorithm in 84.45% of the datasets. Furthermore, the proposal was statistically equivalent to the ground truth when the best CNN is recommended, i.e., when MtL does not select the best CNN, it selects a competitive algorithm. These results show that the proposal was able to extract representative features from image datasets.

关键词： Dataset embedding algorithm selection Meta-learning Image dataset description

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Online metaheuristic algorithm selection

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷 1页

作者： Meidani, Kazem Mirjalili, Seyedali Farimani, Amir Barati Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15232 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA

The performance of optimization algorithms significantly depends on the landscape of the problems. It is known that there is no single algorithm that outperforms others on problems with different fitness landscapes. One of the issues in metaheuristic algorithms is keeping the balance between exploration and exploitation. The features extracted from analysis of fitness landscapes can be used to select the suitable algorithm for the given problem. However, these features are usually expensive and extracted prior to the optimization process which leads to a single algorithm to be selected. In this work, we propose an intelligent switch mechanism that enjoys an efficient non-convex ratio (ENCR) feature extracted online during the optimization to switch between two choices of algorithms, each favoring a type of landscape in terms of modality. For this work, two case studies including a pair of Harris hawks optimizer (HHO) and differential evolution (DE) and another pair of multiverse optimizer (MVO) and moth-flame optimizer (MFO) are selected among several algorithms to evaluate the performance of this framework. The proposed one-way and two-way switch algorithms take advantage of the merits of the two base algorithms to reach better final solutions and higher convergence rates in the majority of case studies. The overall comparison and ranking of the algorithms, including a random switch baseline, demonstrates the superiority of the intelligent switch mechanisms over the baselines.

关键词： Metaheuristic optimization Fitness landscape analysis Efficient non-convex ratio algorithm selection Intelligent switch mechanism Adaptive algorithm Optimization algorithm Benchmark

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Match Field based algorithm selection Approach in Hybrid SUN and PCE Based Optical Networks

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第12期12卷 5723-5743页

作者： Selvaraj, P. Nagarajan, V SRM Univ Sch Comp Kattankulathur India

The evolving internet-based services demand high-speed data transmission in conjunction with scalability. The next generation optical network has to exploit artificial intelligence and cognitive techniques to cope with the emerging requirements. This work proposes a novel way to solve the dynamic provisioning problem in optical network. The provisioning in optical network involves the computation of routes and the reservation of wavelenghs (Routing and Wavelength assignment -RWA). This is an extensively studied multi-objective optimization problem and its complexity is known to be NP-Complete. As the exact algorithms incurs more running time, the heuristic based approaches have been widely preferred to solve this problem. Recently the software-defined networking has impacted the way the optical pipes are configured and monitored. This work proposes the dynamic selection of path computation algorithms in response to the changing service requirements and network scenarios. A software-defined controller mechanism with a novel packet matching feature was proposed to dynamically match the traffic demands with the appropriate algorithm. A software-defined controller with Path Computation Element-PCE was created in the ONOS tool. A simulation study was performed with the case study of dynamic path establishment in ONOS-Open Network Operating System based software defined controller environment. A java based NOX controller was configured with a parent path computation element. The child path computation elements were configured with different path computation algorithms under the control of the parent path computation element. The use case of dynamic bulk path creation was considered. The algorithm selection method is compared with the existing single algorithm based method and the results are analyzed.

关键词： Software Defined Networking-SDN algorithm selection Path Computation Element-PCE

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Instance space analysis and algorithm selection for the job shop scheduling problem

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COMPUTERS & OPERATIONS RESEARCH 2022年 141卷 1页

作者： Strassl, Simon Musliu, Nysret TU Wien Christian Doppler Lab Artificial Intelligence & O DBAI Vienna Austria

This paper is concerned with the job shop scheduling problem, a well-known, NP-hard problem that has been extensively studied in the literature, but for which, despite its age and popularity, there has been relatively little work done towards understanding the landscape of instances. We provide a systematic analysis of the instance space for this problem. For this purpose, the benchmark instances commonly used in the literature were analyzed and extended by a set of newly generated instances of various sizes with processing times drawn from different probability distributions. A number of different state-of-the-art algorithms were evaluated on the extended instance set to analyze their performance patterns and highlight the differences to the current set of benchmark instances. It was found that the existing instances cover a significantly smaller area than the generated ones and did in fact result in different conclusions regarding the algorithms' performances. Furthermore, different algorithms have been shown to excel on distinct subsets of the extended instance set. This has been utilized to train machine learning models to predict the best algorithm for a given instance, the best of which was able to obtain the best solution for 90% of the instances, whereas the best individual algorithm only obtained the best solution for 64%.

关键词： Instance space analysis ISA Job shop scheduling Automated algorithm selection algorithm selection Job scheduling JSSP

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Automated algorithm selection using meta-learning and pre-trained deep convolution neural networks

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INFORMATION FUSION 2024年 105卷

作者： Dagan, Itai Vainshtein, Roman Katz, Gilad Rokach, Lior Ben Gurion Univ Negev Dept Software & Informat Syst Engn POB 653 Beer Sheva Israel

Automated machine learning (AutoML), which explores the automation of various machine learning tasks, has seen significant progress in recent years. One of the fundamental tasks in this field is the selection of effective algorithms for a given dataset. This task is particularly challenging for tabular datasets where, unlike images or text, one cannot use pre-trained embeddings from other datasets. In this study, we present AutoIRAD, a novel meta-learning and vision-based approach for automatic classification algorithm selection for tabular datasets. Our approach is the first to generate image-based representations of entire tabular datasets, enabling us to model the features and interactions of all the dataset samples. Our image-based representation has two significant advantages. First, training our model across multiple datasets enables it to simultaneously learn from diverse domains and create more generalized and effective dataset representations. This representation can also be effectively applied to previously unseen datasets. Secondly, using images enables us to leverage large pre-trained networks (e.g., VGG). By tuning existing networks rather than training new ones, we can both speed up the training of our model and reduce the needed amounts of training data. An extensive evaluation of 150 datasets shows that AutoIRAD achieves results comparable to the leading state-of-the-art AutoML solution but at a fraction of their computation time.

关键词： Automated machine learning Meta-learning algorithm selection Convolutional neural network Machine learning Model recommendation

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AHP based Classification algorithm selection for Clinical Decision Support System Development

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Procedia Computer Science 2014年 36卷 328-334页

作者： Sina Khanmohammadi Mandana Rezaeiahari Department of Systems Science and Industrial Engineering Watson School of Engineering State University of New York at Binghamton Binghamton New York 13902-6000 U.S.A

Supervised classification algorithms have become very popular because of their potential application in developing intelligent data analytic software. These algorithms are known to be sensitive to the characteristic and structure of input datasets, therefore, researchers use different algorithm selection methods to select the most suitable classification algorithm for specific dataset. These methods do not consider the uncertainty about input dataset, and relative importance of different performance measurements (such as speed, accuracy, and memory usage) in the target application domain. Therefore, these methods are not appropriate for software development. This is especially true in medical field where various high dimensional noisy data might be used with the software. Hence, software developers need to select one supervised classification algorithm that has the highest potential to provide good performance in wide variety of datasets. In this regard, an Analytic Hierarchy Process (AHP) based meta-learning algorithm is proposed to identify the most suitable supervised classification algorithm for developing clinical decision support system (CDSS). The results from ten publicly available medical datasets indicate that Support Vector Machine (SVM) has the highest potential to perform well on variety of medical datasets.

关键词： Clinical Decision Support System (CDSS) Machine Learning Medical Informatics algorithm selection AHP Meta-Learning

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Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

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ADVANCED ENGINEERING INFORMATICS 2022年 53卷

作者： Liu, Li Liu, Jian Zhou, Qichao Huang, De Liaoning Tech Univ Coll Safety Sci & Engn Huludao 125105 Peoples R China Minist Educ Key Lab Mine Thermo mot Disaster & Prevent Huludao 125105 Peoples R China Univ South China Sch Resource Environm & Safety Engn Hengyang 421001 Peoples R China Liaoning Tech Univ Huludao 125105 Liaoning Peoples R China

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.

关键词： Mine ventilation Fault diagnosis Machine learning algorithm selection Windage alteration fault

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Reinforcement-based Method for Simultaneous Clustering algorithm selection and its Hyperparameters Optimization

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Procedia Computer Science 2018年 136卷 144-153页

作者： Viacheslav Shalamov Valeria Efimova Sergey Muravyov Andrey Filchenkov ITMO University 49 Kronverksky pr. St Petersburg 197101 Russia

A wide range of clustering algorithms exists, most of them expose many hyperparameters, on which clustering partition quality depends. Simultaneous algorithm (model) selection and its hyperparameters optimization is considered to be a sophisticated task, which is known according to some sources as combined algorithm selection and hyperparameter optimization. In this paper, we focus on problem of selecting a clustering algorithm and its hyperparameter vector simultaneously given a dataset in order to achieve the best partition quality. We propose a method for selecting a proper clustering algorithm and its hyperparameter vector using reinforcement learning. Instead of tuning hyperparameters for all available clustering algorithms and selecting one showing the best performance, we make them to compete for time that they can use for optimizing their own hyperparameters. In our algorithm, we use a framework for multi-armed bandit problem, which is a special case of reinforcement learning. Each clustering algorithm is considered as an arm in the multi-armed bandit setting, while assigning a time budget to optimize hyperparameters of a clustering algorithm is considered as playing the corresponding arm. We conducted series of experiments for comparing out reinforcement learning approach to the classical exhaustive search approach. We conducted experiments on 20 datasets from UCI Repository such as Iris, haberman, krvskp, glass and other. We use 19 cluster validity indices to validate the clusters, built by selected and configured algorithm. As a hyperparameter optimization algorithm, we used SMAC. Our approach managed to improve model selection and hyperparameter optimization process, by sustaining the exploration-exploitation trade-off and spending available time budget more wisely.

关键词： clustering algorithm selection hyperparameter optimization multi-armed bandit reinforcement learning

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