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MACHINE LEARNING 2023年第4期112卷 1253-1286页

作者： Tornede, Alexander Gehring, Lukas Tornede, Tanja Wever, Marcel Huellermeier, Eyke Paderborn Univ Heinz Nixdorf Inst D-33098 Paderborn Germany Univ Munich LMU D-80538 Munich Germany

The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection. Over the past decade, the problem has received considerable attention, resulting in a number of different methods for algorithm selection. Although most of these methods are based on machine learning, surprisingly little work has been done on meta learning, that is, on taking advantage of the complementarity of existing algorithm selection methods in order to combine them into a single superior algorithm selector. In this paper, we introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors. We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework, essentially combining ideas of meta learning and ensemble learning. In an extensive experimental evaluation, we demonstrate that ensembles of algorithm selectors can significantly outperform single algorithm selectors and have the potential to form the new state of the art in algorithm selection.

关键词： algorithm selection Meta learning Ensemble learning Bagging Boosting Stacking

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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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algorithm selection in bilateral negotiation

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AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS 2016年第4期30卷 697-723页

作者： Ilany, Litan Gal, Ya'akov Ben Gurion Univ Negev Dept Informat Syst Engn IL-84105 Beer Sheva Israel

Despite the abundance of strategies in the multi-agent systems literature on repeated negotiation under incomplete information, there is no single negotiation strategy that is optimal for all possible domains. Thus, agent designers face an "algorithm selection" problem-which negotiation strategy to choose when facing a new domain and unknown opponent. Our approach to this problem is to design a "meta-agent" that predicts the performance of different negotiation strategies at run-time. We study two types of the algorithm selection problem in negotiation: In the off-line variant, an agent needs to select a negotiation strategy for a given domain but cannot switch to a different strategy once the negotiation has begun. For this case, we use supervised learning to select a negotiation strategy for a new domain that is based on predicting its performance using structural features of the domain. In the on-line variant, an agent is allowed to adapt its negotiation strategy over time. For this case, we used multi-armed bandit techniques that balance the exploration-exploitation tradeoff of different negotiation strategies. Our approach was evaluated using the GENIUS negotiation test-bed that is used for the annual international Automated Negotiation Agent Competition which represents the chief venue for evaluating the state-of-the-art multi-agent negotiation strategies. We ran extensive simulations using the test bed with all of the top-contenders from both off-line and on-line negotiation tracks of the competition. The results show that the meta-agent was able to outperform all of the finalists that were submitted to the most recent competition, and to choose the best possible agent (in retrospect) for more settings than any of the other finalists. This result was consistent for both off-line and on-line variants of the algorithm selection problem. This work has important insights for multi-agent systems designers, demonstrating that "a little learning goes a long way", de

关键词： Multi-agent negotiation under incomplete information Empirical methods GENIUS framework algorithm selection

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algorithm selection and instance space analysis for curriculum-based course timetabling

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JOURNAL OF SCHEDULING 2022年第1期25卷 35-58页

作者： De Coster, Arnaud Musliu, Nysret Schaerf, Andrea Schoisswohl, Johannes Smith-Miles, Kate TU Wien Inst Log & Computat Database & Artificial Intelligence Grp Vienna Austria TU Wien Christian Doppler Lab Artificial Intelligence & O Vienna Austria Univ Udine DPIA Udine Italy Univ Melbourne Sch Math & Stat Melbourne Vic Australia

We propose an algorithm selection approach and an instance space analysis for the well-known curriculum-based course timetabling problem (CB-CTT), which is an important problem for its application in higher education. Several state of the art algorithms exist, including both exact and metaheuristic methods. Results of these algorithms on existing instances in the literature show that there is no single algorithm outperforming the others. Therefore, a deep analysis of the strengths and weaknesses of these algorithms, depending on the instance, is an important research question. In this work, a detailed analysis of the instance space for CB-CTT is performed, charting the regions where these algorithms perform best. We further investigate the application of machine learning methods to automated algorithm selection for CB-CTT, strengthening the insights gained through the instance space analysis. For our research, we contribute new real-life instances and extend the generation of synthetic instances to better correspond to these new instances. Finally, this work shows how instance space analysis and the application of algorithm selection complement each other, underlining the value of both approaches in understanding algorithm performance.

关键词： Timetabling Scheduling algorithm selection Classification Instance space Instance generation

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algorithm selection for black-box continuous optimization problems: A survey on methods and challenges

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INFORMATION SCIENCES 2015年 317卷 224-245页

作者： Munoz, Mario A. Sun, Yuan Kirley, Michael Halgamuge, Saman K. Monash Univ Sch Math Sci Clayton Vic 3800 Australia Univ Melbourne Dept Mech Engn Parkville Vic 3010 Australia Univ Melbourne Dept Comp & Informat Syst Parkville Vic 3010 Australia

Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed. In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice's (1976) selection framework. We describe each of the four component spaces - problem, algorithm, performance and characteristic - in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions. (C) 2015 Elsevier Inc. All rights reserved.

关键词： algorithm selection Black-box continuous optimization Empirical performance models Exploratory landscape analysis Performance prediction Problem hardness measures

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algorithm selection for Software Verification Using Graph Neural Networks

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第3期33卷 1-36页

作者： Leeson, Will Dwyer, Matthew B. Univ Virginia 85 Engineers Way Charlottesville VA 22903 USA

The field of software verification has produced a wide array of algorithmic techniques that can prove a variety of properties of a given program. It has been demonstrated that the performance of these techniques can vary up to 4 orders of magnitude on the same verification problem. Even for verification experts, it is difficult to decide which tool will perform best on a given problem. For general users, deciding the best tool for their verification problem is effectively impossible. In this work, we present GRAVES, a selection strategy based on graph neural networks (GNNs). GRAVES generates a graph representation of a program from which a GNN predicts a score for a verifier that indicates its performance on the program. We evaluate GRAVES on a set of 10 verification tools and over 8,000 verification problems and find that it improves the state-of-the-art in verification algorithm selection by 12%, or 8 percentage points. Further, it is able to verify 9% more problems than any existing verifier on our test set. Through a qualitative study on model interpretability, we find strong evidence that the GRAVES model learns to base its predictions on factors that relate to the unique features of the algorithmic techniques.

关键词： algorithm selection graph neural networks

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algorithm selection of Anytime algorithms

Algorithm Selection of Anytime Algorithms

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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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algorithm selection for Dynamic Symbolic Execution: A Preliminary Study 30th

Algorithm Selection for Dynamic Symbolic Execution: A Prelim...

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30th International Conference on Logic-Based Program Synthesis and Transformation (LOPSTR)

作者： Amadini, Roberto Gange, Graeme Schachte, Peter Sondergaard, Harald Stuckey, Peter J. Univ Bologna Bologna Italy Univ Melbourne Parkville Vic Australia Monash Univ Clayton Vic Australia

ISBN: (纸本)9783030684457;9783030684464

Given a portfolio of algorithms, the goal of algorithm selection (AS) is to select the best algorithm(s) for a new, unseen problem instance. Dynamic Symbolic Execution (DSE) brings together concrete and symbolic execution to maximise the program coverage. DSE uses a constraint solver to solve the path conditions and generate new inputs to explore. In this paper we join these lines of research by introducing a model that combines DSE and AS approaches. The proposed AS/DSE model is a generic and flexible framework enabling the DSE engine to solve the path conditions it collects with a portfolio of different solvers, by exploiting and extending the well-known AS techniques that have been developed over the last decade. In this way, one can increase the coverage and sometimes even outperform the aggregate coverage achievable by running simultaneously all the solvers of the portfolio.

关键词： Software verification Dynamic symbolic execution algorithm selection Constraint solving Portfolio solving

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algorithm selection Using Deep Learning Without Feature Extraction

Algorithm Selection Using Deep Learning Without Feature Extr...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Alissa, Mohamad Sim, Kevin Hart, Emma Edinburgh Napier Univ Dept Comp Sci Edinburgh Midlothian Scotland

ISBN: (纸本)9781450361118

We propose a novel technique for algorithm-selection which adopts a deep-learning approach, specifically a Recurrent-Neural Network with Long-Short-Term-Memory (RNN-LSTM). In contrast to the majority of work in algorithm-selection, the approach does not need any features to be extracted from the data but instead relies on the temporal data sequence as input. A large case-study in the domain of 1-d bin packing is undertaken in which instances can be solved by one of four heuristics. We first evolve a large set of new problem instances that each have a clear "best solver" in terms of the heuristics considered. An RNN-LSTM is trained directly using the sequence data describing each instance to predict the best-performing heuristic. Experiments conducted on small and large problem instances with item sizes generated from two different probability distributions are shown to achieve between 7% to 11% improvement over the single best solver (SBS) (i.e. the single heuristic that achieves the best performance over the instance set) and 0% to 2% lower than the virtual best solver (VBS), i.e the perfect mapping.

关键词： Deep Learning Recurrent Neural Network algorithm selection

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algorithm selection for Classification Problems via Cluster-based Meta-features

Algorithm Selection for Classification Problems via Cluster-...

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IEEE International Conference on Big Data (Big Data)

作者： Ler, Daren Teng, Hongyu He, Yu Gidijala, Rahul Minist Educ Singapore Singapore Natl Jr Coll Singapore Singapore

ISBN: (纸本)9781538650356

Meta-features describe the characteristics of the datasets to facilitate algorithm selection. This paper proposes a new set of meta-features based on clustering the instances within datasets. We propose the use of a greedy clustering algorithm, and evaluate the meta-features generated based on the learning curves produced by the Random Forest algorithm. We also compared the utility of the proposed meta-features against preexisting meta-features described in the literature, and evaluated the applicability of these meta-features over a sample of UCI datasets. Our results show that these meta-features do indeed improve the performance when applied to the algorithm selection task.

关键词： meta-learning algorithm selection cluster-based meta-features

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