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Taking the human out of decomposition-based optimization via artificial intelligence, Part I: Learning when to decompose

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COMPUTERS & CHEMICAL ENGINEERING 2024年 186卷

作者： Mitrai, Ilias Daoutidis, Prodromos Univ Minnesota Dept Chem Engn & Mat Sci Minneapolis MN 55455 USA

In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition -based solution method. In this approach, an optimization problem is represented as a graph that captures the structural and functional coupling among the variables and constraints of the problem via an appropriate set of features. Given this representation, a graph classifier can be built to assist a solver in selecting the best solution strategy for a given problem with respect to some metric of choice. The proposed approach is used to develop a classifier that determines whether a convex Mixed Integer Nonlinear Programming problem should be solved using branch and bound or the outer approximation algorithm. Finally, it is shown how the learned classifier can be incorporated into existing mixed integer optimization solvers.

关键词： algorithm selection Decomposition-based solution algorithms Deep learning Graph neural networks Convex MINLP Graph classification

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A Survey on AutoML Methods and Systems for Clustering

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2024年第5期18卷 1-30页

作者： Poulakis, Yannis Doulkeridis, Christos Kyriazis, Dimosthenis Univ Piraeus Dept Digital Syst Karaoli & Dimitriou Str 80 Piraeus 18534 Greece

Automated Machine Learning (AutoML) aims to identify the best-performing machine learning algorithm along with its input parameters for a given dataset and a specific machine learning task. This is a challenging problem, as the process of finding the best model and tuning it for a particular problem at hand is both time-consuming for a data scientist and computationally expensive. In this survey, we focus on unsupervised learning, and we turn our attention on AutoML methods for clustering. We present a systematic review that includes many recent research works for automated clustering. Furthermore, we provide a taxonomy for the classification of existing works, and we perform a qualitative comparison. As a result, this survey provides a comprehensive overview of the field of AutoML for clustering. Moreover, we identify open challenges for future research in this field.

关键词： Unsupervised learning clustering automated machine learning metalearning algorithm selection hyperparameter tuning

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Which algorithm to select in sports timetabling?

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第2期318卷 575-591页

作者： Van Bulck, David Goossens, Dries Clarner, Jan-Patrick Dimitsas, Angelos Fonseca, George H. G. Lamas-Fernandez, Carlos Lester, Martin Mariusz Pedersen, Jaap Phillips, Antony E. Rosati, Roberto Maria Univ Ghent Fac Econ & Business Adm Tweekerkenstr 2 B-9000 Ghent Belgium FlandersMake UGent Core Lab CVAMO Ghent Belgium Zuse Inst Berlin Appl Algorithm Intelligence Methods Dept Takustr 7 D-14195 Berlin Germany Univ Ioannina Dept Informat Ioannina Greece Univ Fed Ouro Preto Comp & Syst Dept R Diogo Vasconcelos 122 Ouro Preto Brazil Univ Southampton CORMSIS Ctr Operat Res Management Sci & Informat S Southampton Business Sch Southampton England Univ Reading Dept Comp Sci Reading England 7bridges 23 Meard St London England Univ Udine DPIA Via Sci 206 I-33100 Udine Italy

Any sports competition needs a timetable, specifying when and where teams meet each other. The recent International Timetabling Competition (ITC2021) on sports timetabling showed that, although it is possible to develop general algorithms, the performance of each algorithm varies considerably over the problem instances. This paper provides a problem type analysis for sports timetabling, resulting in powerful insights into the strengths and weaknesses of eight state-of-the-art algorithms. Based on machine learning techniques, we propose an algorithm selection system that predicts which algorithm is likely to perform best based on the type of competition and constraints being used (i.e., the problem type) in a given sports timetabling problem instance. Furthermore, we visualize how the problem type relates to algorithm performance, providing insights and possibilities to further enhance several algorithms. Finally, we assess the empirical hardness of the instances. Our results are based on large computational experiments involving about 50 years of CPU time on more than 500 newly generated problem instances.

关键词： OR in sports Sports scheduling ITC2021 algorithm selection Instance space analysis

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Selecting fast algorithms for the capacitated vehicle routing problem with machine learning techniques

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NETWORKS 2024年第4期84卷 465-480页

作者： Asin-Acha, Roberto Espinoza, Alexis Goldschmidt, Olivier Hochbaum, Dorit S. Huerta, Isaias I. Univ Tecn Federico Santa Maria Dept Informat Valparaiso Chile Univ Concepcion Dept Comp Sci Concepcion Chile Riverside Cty Off Educ Riverside CA USA Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA USA

We present machine learning (ML) methods for automatically selecting a "best" performing fast algorithm for the capacitated vehicle routing problem (CVRP) with unit demands. algorithm selection is to automatically choose among a portfolio of algorithms the one that is predicted to work best for a given problem instance, and algorithm configuration is to automatically select algorithm's parameters that are predicted to work best for a given problem instance. We present a framework incorporating both algorithm selection and configuration for a portfolio that includes the automatically configured "Sweep algorithm," the first generated feasible solution of the hybrid genetic search algorithm, and the Clarke and Wright algorithm. The automatically selected algorithm is shown here to deliver high-quality feasible solutions within very small running times making it highly suitable for real-time applications and for generating initial feasible solutions for global optimization methods for CVRP. These results bode well to the effectiveness of utilizing ML for improving combinatorial optimization methods.

关键词： algorithm selection machine learning vehicle routing

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Sibyl: Improving Software Engineering Tools with SMT selection 23

Sibyl: Improving Software Engineering Tools with SMT Selecti...

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45th IEEE/ACM International Conference on Software Engineering (ICSE)

作者： Leeson, Will Dwyer, Matthew B. Filieri, Antonio Univ Virginia Dept Comp Sci Charlottesville VA 22904 USA Imperial Coll London Dept Comp London England

ISBN: (纸本)9781665457019

SMT solvers are often used in the back end of different software engineering tools-e.g., program verifiers, test generators, or program synthesizers. There are a plethora of algorithmic techniques for solving SMT queries. Among the available SMT solvers, each employs its own combination of algorithmic techniques that are optimized for different fragments of logics and problem types. The most efficient solver can change with small changes in the SMT query, which makes it nontrivial to decide which solver to use. Consequently, designers of software engineering tools often select a single solver, based on familiarity or convenience, and tailor their tool towards it. Choosing an SMT solver at design time misses the opportunity to optimize query solve times and, for tools where SMT solving is a bottleneck, the performance loss can be significant. In this work, we present Sibyl, an automated SMT selector based on graph neural networks (GNNs). Sibyl creates a graph representation of a given SMT query and uses GNNs to predict how each solver in a suite of SMT solvers would perform on said query. Sibyl learns to predict based on features of SMT queries that are specific to the population on which it is trained - avoiding the need for manual feature engineering. Once trained, Sibyl makes fast and accurate predictions which can substantially reduce the time needed to solve a set of SMT queries. We evaluate Sibyl in four scenarios in which SMT solvers are used: in competition, in a symbolic execution engine, in a bounded model checker, and in a program synthesis tool. We find that Sibyl improves upon the state of the art in nearly every case and provide evidence that it generalizes better than existing techniques. Further, we evaluate Sibyl's overhead and demonstrate that it has the potential to speedup a variety of different software engineering tools.

关键词： graph neural networks satisfiable modulo theories algorithm selection

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Eight years of AutoML: categorisation, review and trends

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KNOWLEDGE AND INFORMATION SYSTEMS 2023年第12期65卷 5097-5149页

作者： Barbudo, Rafael Ventura, Sebastian Romero, Jose Raul Univ Cordoba Dept Comp Sci & Numer Anal Cordoba 14071 Spain Andalusian Res Inst Data Sci & Computat Intelligen Cordoba Spain

Knowledge extraction through machine learning techniques has been successfully applied in a large number of application domains. However, apart from the required technical knowledge and background in the application domain, it usually involves a number of time-consuming and repetitive steps. Automated machine learning (AutoML) emerged in 2014 as an attempt to mitigate these issues, making machine learning methods more practicable to both data scientists and domain experts. AutoML is a broad area encompassing a wide range of approaches aimed at addressing a diversity of tasks over the different phases of the knowledge discovery process being automated with specific techniques. To provide a big picture of the whole area, we have conducted a systematic literature review based on a proposed taxonomy that permits categorising 447 primary studies selected from a search of 31,048 papers. This review performs an extensive and rigorous analysis of the AutoML field, scrutinising how the primary studies have addressed the dimensions of the taxonomy, and identifying any gaps that remain unexplored as well as potential future trends. The analysis of these studies has yielded some intriguing findings. For instance, we have observed a significant growth in the number of publications since 2018. Additionally, it is noteworthy that the algorithm selection problem has gradually been superseded by the challenge of workflow composition, which automates more than one phase of the knowledge discovery process simultaneously. Of all the tasks in AutoML, the growth of neural architecture search is particularly noticeable.

关键词： AutoML algorithm selection Hyper-parameter optimisation Neural architecture search Workflow composition Systematic literature review

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ASAP: An Automatic algorithm selection Approach for Planning

ASAP: An Automatic Algorithm Selection Approach for Planning

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25th Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Vallati, Mauro Chrpa, Lukas Kitchin, Diane Univ Huddersfield Sch Comp & Engn PK Res Grp Huddersfield HD1 3DH W Yorkshire England

Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic algorithm selection Approach for Planning that (i) for a given domain initially learns additional knowledge, in the form of macro operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings-planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans.

关键词： Automated planning algorithm selection learning for planning

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Combination and selection of Machine Learning algorithms in GNSS Architecture Design for Concurrent Executions with HIL Testing 42

Combination and Selection of Machine Learning Algorithms in ...

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IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC)

作者： Xu, Zhengjia Petrunin, Ivan Tsourdos, Antonios Grech, Raphael Peltola, Pekka Tiwari, Smita Cranfield Univ Autonomous & Cyber Phys Sys Cranfield Beds England Spirent Commun Plc Spirent Crawley Devon England Telespazio UK Nav Luton Beds England

ISBN: (纸本)9798350333572

As machine learning (ML) continuing to gain popularity, ML-assisted Global Navigation Satellite System (GNSS) receivers facilitate the performance of Autonomous Systems (AS) navigation solutions. However, selections of ML is often a trade-off in practice where empirical knowledge is taken to alleviate complexities. Therefore, this paper explores decision-making solutions for maximising determined hardware performance under quantitative and qualitative considerations. This work proposes algorithm selection and Matching with Fuzzy Analytic Hierarchy Process (ASM-FAHP) that maps multiple trade-off concerns into a Multi-Criteria Decision-Making (MCDM) problem. The ASM-FAHP firstly searches all the possible alternatives to find possible combinations with hardware resource limitations taken into account. Afterwards, ASM-FAHP prioritizes the most significant candidate by constructing a hierarchical structure with several attributes and scoring with fuzzy numbers. Hereby, the most suitable ML combinations are determined by calculating synthesised fuzzy weights per each alternative. The performance of the ML combination is evaluated by concurrently executing it on resource-constrained hardware, specifically the Jetson Nano board. The ML models are trained and tested using high-fidelity synthetic datasets produced from Spirent GSS7000 simulator and SimGen while connected to hardware-in-the-loop (HIL). It has been discovered that when approaching hardware limits, the selected combination of machine learning algorithms makes full use of memory resources but sacrifices processing speed.

关键词： GNSS receiver design machine learning deep learning algorithm selection FAHP

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Multi-armed bandits with censored consumption of resources

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MACHINE LEARNING 2023年第1期112卷 217-240页

作者： Bengs, Viktor Huellermeier, Eyke Ludwig Maximilians Univ Munchen Inst Informat Munich Germany Munich Ctr Machine Learning Munich Germany

We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of consumed resources remains below the limit. Otherwise, the observation is censored, i.e., no reward is obtained. For this problem setting, we introduce a measure of regret, which incorporates both the actual amount of consumed resources of each learning round and the optimality of realizable rewards as well as the risk of exceeding the allocated resource limit. Thus, to minimize regret, the learner needs to set a resource limit and choose an arm in such a way that the chance to realize a high reward within the predefined resource limit is high, while the resource limit itself should be kept as low as possible. We propose a UCB-inspired online learning algorithm, which we analyze theoretically in terms of its regret upper bound. In a simulation study, we show that our learning algorithm outperforms straightforward extensions of standard multi-armed bandit algorithms.

关键词： algorithm selection Bivariate feedback Censored feedback Exploration Exploitation

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An Instance Space Analysis of Constrained Multiobjective Optimization Problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第5期27卷 1427-1439页

作者： Alsouly, Hanan Kirley, Michael Munoz, Mario Andres Univ Melbourne Sch Comp & Informat Melbourne Vic 3010 Australia ARC Ctr Optimisat Technol Integrated Methodol & A Melbourne Vic 3010 Australia Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Riyadh 11564 Saudi Arabia

Constrained multiobjective optimization problems (CMOPs) are generally more challenging than unconstrained problems. This in part can be attributed to the infeasible region generated by the constraint functions, the interaction between constraints and objectives, or both. In this article, we explore the relationship between the performance of constrained multiobjective evolutionary algorithms (CMOEAs) and the instance characteristics of CMOP using instance space analysis (ISA). To do this, we extend recent work on Landscape Analysis features for characterizing CMOPs. Specifically, we introduce new features to describe the multiobjective-violation landscape, formed by the interaction between constraint violation and multiobjective fitness. The detailed evaluation of the algorithm footprints, spanning eight CMOP benchmark suites and 15 CMOEAs, demonstrates that ISA effectively captures the strength and weakness of the CMOEAs. We conclude that two characteristics, the isolation of nondominate set and the correlation between constraints and objectives evolvability, have the greatest impact on algorithm performance. However, the current benchmarks problems lack of diversity to represent the real-world problems and to fully reveal the efficacy of CMOEAs evaluated.

关键词： algorithm selection constrained multiobjective optimization evolutionary algorithm landscape analysis problem characterization

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