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Genetic and Evolutionary Computation Conference (GECCO)

作者： Jesus, Alexandre D. Liefooghe, Arnaud Derbel, Bilel Paquete, Luis Univ Lille INRIA Cent Lille CNRSUMR 9189 CRIStAL F-59000 Lille France Univ Tokyo JFLI CNRS IRL 3527 Tokyo Japan Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9781450371285

Anytime algorithms for optimization problems are of particular interest since they allow to trade off execution time with result quality. However, the selection of the best anytime algorithm for a given problem instance has been focused on a particular budget for execution time or particular target result quality. Moreover, it is often assumed that these anytime preferences are known when developing or training the algorithm selection methodology. In this work, we study the algorithm selection problem in a context where the decision maker's anytime preferences are defined by a general utility function, and only known at the time of selection. To this end, we first examine how to measure the performance of an anytime algorithm with respect to this utility function. Then, we discuss approaches for the development of selection methodologies that receive a utility function as an argument at the time of selection. Then, to illustrate one of the discussed approaches, we present a preliminary study on the selection between an exact and a heuristic algorithm for a bi-objective knapsack problem. The results show that the proposed methodology has an accuracy greater than 96% in the selected scenarios, but we identify room for improvement.

关键词： algorithm selection Anytime algorithms Anytime Performance Measure

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R-CASS: Using algorithm selection for Self-Adaptive Service Oriented Systems

R-CASS: Using Algorithm Selection for Self-Adaptive Service ...

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IEEE International Conference on Web Services (ICWS) / IEEE World Congress on Services (IEEE SERVICES)

作者： Deshpande, Niranjana Sharma, Naveen Yu, Qi Krutz, Daniel E. Rochester Inst Technol B Thomas Golisano Coll Comp & Informat Sci Rochester NY 14623 USA

ISBN: (纸本)9781665416818

In service composition, complex applications are built by combining web services to fulfill user Quality of Service (QoS) and business requirements. To meet these requirements, applications are composed by evaluating all possible web service combinations using search algorithms. These algorithms need to be accurate and inexpensive to evaluate a large number of possible service combinations and services' fluctuating QoS attributes while meeting the constraints of limited computational resources. Recent research has shown that different search algorithms can outperform others on specific instances of a problem domain, in terms of solution quality and computational resource usage. Problematically, current service composition approaches ignore this property, leading to inefficient compositions. To address these limitations, we propose a composition algorithm selection framework which selects an algorithm per composition task at runtime, R-CASS. Our evaluations demonstrate that R-CASS leads to more efficient compositions, reducing composition time by 55.1% and memory by 37.5%.

关键词： Service Composition algorithm selection Runtime Adaptation MAPE-K Composition algorithm Adaptation Supervised Learning

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TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection 21

TRIO: Task-agnostic dataset representation optimized for aut...

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21st IEEE International Conference on Data Mining (IEEE ICDM)

作者： Cohen-Shapira, Noy Rokach, Lior Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9781665423984

With the growing number of machine learning (ML) algorithms, the selection of the top-performing algorithms for a given dataset, task, and evaluation measure is known to be a challenging task. The human expertise required for this task has fueled the demand for automatic solutions. Meta-learning is a popular approach for automatic algorithm selection based on dataset characterization. Existing meta-learning methods often represent the datasets using predefined features and thus cannot be generalized for various ML tasks, or alternatively, learn their representations in a supervised fashion, and thus cannot address unsupervised tasks. In this study, we first propose a novel learning-based task-agnostic method for dataset representation. Second, we present TRIO, a meta-learning approach based on the proposed dataset representation, which is capable of accurately recommending top-performing algorithms for unseen datasets. TRIO first learns graphical representations from the datasets and then utilizes a graph convolutional neural network technique to extract their latent representations. An extensive evaluation on 337 datasets and 195 ML algorithms demonstrates the effectiveness of our approach over state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.

关键词： AutoML algorithm selection meta-learning task-agnostic dataset representation

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algorithm selection Framework for Cyber Attack Detection 2

Algorithm Selection Framework for Cyber Attack Detection

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2nd ACM Workshop on Wireless Security and Machine Learning (WiseML)

作者： Chale, Marc Bastian, Nathaniel D. Weir, Jeffery Air Force Inst Technol Wright Patterson AFB OH 45433 USA Army Cyber Inst West Point NY USA

ISBN: (纸本)9781450380072

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.

关键词： machine learning algorithm selection meta learning feature engineering cybersecurity

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Identifying Easy Instances to Improve Efficiency of ML Pipelines for algorithm-selection 18th

Identifying Easy Instances to Improve Efficiency of ML Pipel...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Renau, Quentin Hart, Emma Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9783031700675;9783031700682

algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely on an analysis phase, e.g. where features are computed by sampling solutions and used as input in a machine-learning model. For AS to be efficient, it is therefore important that this analysis phase is not computationally expensive. We propose a method for identifying easy instances which can be solved quickly using a generalist solver without any need for algorithm-selection. This saves computational budget associated with feature-computation which can then be used elsewhere in an AS pipeline, e.g., enabling additional function evaluations on hard problems. Experiments on the BBOB dataset in two settings (batch and streaming) show that identifying easy instances results in substantial savings in function evaluations. Re-allocating the saved budget to hard problems provides gains in performance compared to both the virtual best solver (VBS) computed with the original budget, the single best solver (SBS) and a trained algorithm-selector.

关键词： algorithm selection Budget Re-Allocation Black-Box Optimisation

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On the use of fitness landscape features in meta-learning based algorithm selection for the quadratic assignment problem

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THEORETICAL COMPUTER SCIENCE 2020年 805卷 62-75页

作者： Dantas, Augusto Pozo, Aurora Univ Fed Parana Dept Comp Sci Curitiba Parana Brazil

Meta-heuristics perform differently depending on the problem instance they are solving, meaning that manually choosing an algorithm is not trivial and an automatic selection is desirable. This task can be addressed using a meta-learning approach, which relates the characteristics of the problem instances to the performance of a set of solving algorithms. Therefore, the success of such approach is based on the quality of the extracted set of features. Some studies have proposed the use of features based on Fitness Landscape Analysis (FLA) to characterize optimization problems. However, extracting measures based on FLA usually requires a high computational effort. In a previous work, we have employed meta-heuristic selection on the Quadratic Assignment Problem (QAP) using some FLA measures. This research extends our study by including additional FLA meta-features, by using a less costly extraction method, and by considering more QAP instances. In total, we built five multi-label datasets, each composed of meta-features that were extracted by different sampling sizes, and then we used them to train Random Forest classifiers. Besides presenting satisfactory classification performance, and a decrease in time consumption in relation to our previous work, this selection approach was able to achieve better solution costs over the set of QAP instances if compared to running the meta-heuristics individually. (C) 2019 Elsevier B.V. All rights reserved.

关键词： algorithm selection Meta-learning Combinatorial optimization Fitness landscape analysis

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algorithm selection in Adversarial Settings: From Experiments to Tournaments in StarCraft

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IEEE TRANSACTIONS ON GAMES 2019年第3期11卷 238-247页

作者： Tavares, Anderson Rocha Vieira, Daniel Kneipp S. Negrisoli, Tiago Chaimowicz, Luiz Univ Fed Minas Gerais Lab Multidisciplinary Res Games BR-31270901 Belo Horizonte MG Brazil

algorithm selection-the mapping of problem instances to algorithms-has been successfully applied to a variety of complex theoretical and practical problems, including computer games. In this paper, we extend the traditional framework, which considers a single decision maker, to adversarial settings, by modeling algorithm selection as a normal-form game. In this "game of algorithm selection," agents select algorithms to play a computer game on their behalf. The game's payoff matrix stores the relative performance among algorithms. We also consider nonstationary scenarios, where algorithms can learn from previous matches. We apply this approach to real-time strategy game StarCraft, using hots developed for the game as our algorithms. Our experiments suggest that minimax-Q is a suitable method for algorithm selection in both stationary and nonstationary conditions. We proceed by implementing our approach in MegaBot, a fully capable StarCraft bot. MegaBot showed robustness to nonstationarity, competing in the difficult scenario of StarCraft AI tournaments. MegaBot successfully learns how to select algorithms, exhibiting increasing win rates with tournament progress. In 2016, its debut year, it left 60% of opponents behind in two out of three tournaments. In 2017, however, MegaBot faced difficulties as its algorithm portfolio got outdated compared to newer entries.

关键词： algorithm selection game theory real-time strategy (RTS) games StarCraft

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algorithm selection Framework: A Holistic Approach to the algorithm selection Problem

Algorithm Selection Framework: A Holistic Approach to the Al...

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作者： Marc W. Chalé Air Force Institute of Technology

学位级别：硕士

A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of the top ranked recommendation is considered excellent with at least 95 percent of the best observed recall; the average of this metric is 79 percent due to two poorly performing recommendations. The top recommendation is Pareto efficient for three of the problems. The framework measures well against an a-priori set of criteria. The framework provides value by filtering the candidate of analytic techniques and, often, selecting a high performing technique as the top ranked recommendation. The user input and meta-data used by the framework contain information with high potential for effective algorithm selection. Future work should optimize the recommendation logic and expand the scope of techniques for other types of analysis problems. Further, the results of this proposed study should be leveraged in order to better understand the behavior of meta-learning models.

关键词： meta-learning machine learning algorithm selection data analysis artificial intelligence

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algorithm selection and combining multiple learners for residential energy prediction

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 99卷 391-400页

作者： Gungor, Onat Aksanli, Baris Aydogan, Reyhan Ozyegin Univ Comp Sci & Engn Dept Istanbul Turkey San Diego State Univ Elect & Comp Engn Dept San Diego CA 92182 USA Delft Univ Technol Interact Intelligence Grp Delft Netherlands

Balancing supply and demand management in energy grids requires knowing energy consumption in advance. Therefore, forecasting residential energy consumption accurately plays a key role for future energy systems. For this purpose, in the literature a number of prediction algorithms have been used. This work aims to increase the accuracy of those predictions as much as possible. Accordingly, we first introduce an algorithm selection approach, which identifies the best prediction algorithm for the given residence with respect to its characteristics such as number of people living, appliances and so on. In addition to this, we also study combining multiple learners to increase the accuracy of the predictions. In our experimental setup, we evaluate the aforementioned approaches. Empirical results show that adopting an algorithm selection approach performs better than any single prediction algorithm. Furthermore, combining multiple learners increases the accuracy of the energy consumption prediction significantly. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Electricity consumption prediction algorithm selection Combining multiple learners Time series prediction

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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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