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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Carnein, Matthias Trautmann, Heike Bifet, Albert Pfahringer, Bernhard Univ Munster Munster Germany Univ Waikato Hamilton New Zealand

ISBN: (纸本)9783030438234;9783030438227

Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today's data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.

关键词： Stream clustering automated algorithm configuration algorithm selection Ensemble techniques

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Golden Parameter Search Exploiting Structure to Quickly Configure Parameters in Parallel

Golden Parameter Search Exploiting Structure to Quickly Conf...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Pushak, Yasha Hoos, Holger H. Univ British Columbia Dept Comp Sci Vancouver BC Canada Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9781450371285

automated algorithm configuration procedures such as SMAC, GGA++ and irace can often find parameter configurations that substantially improve the performance of state-of-the-art algorithms for difficult problems - e.g., a three-fold speedup in the running time required by EAX, a genetic algorithm, to find optimal solutions to a set of widely studied TSP instances. However, it is usually recommended to provide these methods with running time budgets of one or two days of wall-clock time as well as dozens of CPU cores. Most general-purpose algorithm configuration methods are based on powerful meta-heuristics that are designed for challenging and complex search landscapes;however, recent work has shown that many algorithms appear to have parameter configuration landscapes with a relatively simple structure. We introduce the golden parameter search (GPS) algorithm, an automatic configuration procedure designed to exploit this structure while optimizing each parameter semi-independently in parallel. We compare GPS to several state-of-the-art algorithm configurators and show that it often finds similar or better parameter configurations using a fraction of the computing time budget across a broad range of scenarios spanning TSP, SAT and MIP.

关键词： automated algorithm configuration Parallel algorithm configuration Parameter Turning Hyper-Parameter Optimization

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Encoding Adaptability of Software Engineering Tools as algorithm configuration Problem: A Case Study 34

Encoding Adaptability of Software Engineering Tools as Algor...

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34th IEEE/ACM International Conference on automated Software Engineering (ASE)

作者： Basmer, Maike Kehrer, Timo Humboldt Univ Dept Comp Sci Berlin Germany

ISBN: (纸本)9781728141367

Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted configurations are often far from optimal. In this paper, we study the software configuration problem in the context of the model comparison tool SiDiff, which needs to be carefully adapted to domain-specific modeling languages used in model-driven engineering. To tackle the configuration challenge, we propose to draw from the field of automated algorithm configuration, a research area which has studied the optimization of parameterizable algorithms for many years and which has gained particular momentum through its applications to hyper-parameter tuning in machine learning. Specifically, we report on ongoing work encoding the adaptability of SiDiff as an algorithm configuration problem which is amenable to a sequential model-based optimization tool known as SMAC. While empirical evaluation results are left for future work, the main goal of this paper is to foster active discussions at the workshop and to collect early feedback on our ongoing research.

关键词： Software engineering tools adaptability automated algorithm configuration SiDiff SMAC

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automated algorithm Selection: Survey and Perspectives

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EVOLUTIONARY COMPUTATION 2019年第1期27卷 3-45页

作者： Kerschke, Pascal Hoos, Holger H. Neumann, Frank Trautmann, Heike Univ Munster Informat Syst & Stat D-48149 Munster Germany Leiden Univ Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands Univ Adelaide Optimisat & Logist Adelaide SA 5005 Australia

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art;instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

关键词： automated algorithm selection automated algorithm configuration combinatorial optimisation continuous optimisation machine learning metalearning feature-based approaches exploratory landscape analysis data streams

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automated configuration of Negotiation Strategies 20

Automated Configuration of Negotiation Strategies

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Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems

作者： Bram M. Renting Holger H. Hoos Catholijn M. Jonker Delft University of Technology Delft Netherlands Leiden University Leiden Netherlands Delft University of Technology & Leiden University Delft Netherlands

ISBN: (纸本)9781450375184

Bidding and acceptance strategies have a substantial impact on the outcome of negotiations in scenarios with linear additive and nonlinear utility functions. Over the years, it has become clear that there is no single best strategy for all negotiation settings, yet many fixed strategies are still being developed. We envision a shift in the strategy design question from: What is a good strategy?, towards: What could be a good strategy? For this purpose, we developed a method leveraging automated algorithm configuration to find the best strategies for a specific set of negotiation settings. By empowering automated negotiating agents using automated algorithm configuration, we obtain a flexible negotiation agent that can be configured automatically for a rich space of opponents and negotiation *** critically assess our approach, the agent was tested in an ANAC-like bilateral automated negotiation tournament setting against past competitors. We show that our automatically configured agent outperforms all other agents, with a 5.1% increase in negotiation payoff compared to the next-best agent. We note that without our agent in the tournament, the top-ranked agent wins by a margin of only 0.01%.

关键词： automated negotiation automated algorithm configuration negotiation strategy

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Expressiveness and Robustness of Landscape Features 19

Expressiveness and Robustness of Landscape Features

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Renau, Quentin Dreo, Johann Doerr, Carola Doerr, Benjamin THALES Res & Technol Palaiseau France Sorbonne Univ CNRS Paris France Ecole Polytech Palaiseau France

ISBN: (纸本)9781450367486

Insights on characteristics of an optimization problem is highly important in order to select and configure the right algorithm. Some techniques called features are defined for analyzing the fitness landscape of a problem. Despite their successes, our understanding of which features are actually relevant for the discrimination between different optimization problems is rather weak, since in most applications the features are used in a black-box manner. Another aspect that has been ignored in the exploratory landscape analysis literature is the robustness of the feature computation against the randomness of sample points from which the feature values are estimated. Moreover, the influence of the number of sample points from which the feature values are estimated is also an aspect ignored by the literature. In this paper, we study these three aspects: the robustness against the random sampling, the influence of the number of sample points, and the expressiveness in terms of ability to discriminate problems. We perform such an analysis for 7 out of the 17 features sets covered by the flacco package. Our test bed are the 24 noiseless BBOB functions. We show that some of these features seems very well-fitted for the discrimination of the problems and quite robust whereas others lack robustness and/or expressiveness, and are therefore less suitable for an automated landscape-aware algorithm selection/configuration approach.

关键词： Exploratory Landscape Analysis Black-Box Optimization automated algorithm configuration

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Analysing differences between algorithm configurations through ablation

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JOURNAL OF HEURISTICS 2016年第4期22卷 431-458页

作者： Fawcett, Chris Hoos, Holger H. Univ British Columbia Dept Comp Sci 201-2366 Main Mall Vancouver BC V6T 1Z4 Canada

Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated parameter tuning and algorithm configuration tools, and consequently often create solvers with many parameters and vast configuration spaces. However, there has been very little work to help these algorithm developers answer questions about the high-quality configurations produced by these tools, specifically about which parameter changes contribute most to improved performance. In this work, we present an automated technique for answering such questions by performing ablation analysis between two algorithm configurations. We perform an extensive empirical analysis of our technique on five scenarios from propositional satisfiability, mixed-integer programming and AI planning, and show that in all of these scenarios more than 95 % of the performance gains between default configurations and optimised configurations obtained from automated configuration tools can be explained by modifying the values of a small number of parameters (1-4 in the scenarios we studied). We also investigate the use of our ablation analysis procedure for producing configurations that generalise well to previously-unseen problem domains, as well as for analysing the structure of the algorithm parameter response surface near and between high-performance configurations.

关键词： Ablation analysis Parameter importance automated algorithm configuration Empirical analysis

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Towards a Method for Automatic algorithm configuration: A Design Evaluation Using Tuners

Towards a Method for Automatic Algorithm Configuration: A De...

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13th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Montero, Elizabeth Riff, Maria-Cristina Univ Tecn Federico Santa Maria Dept Comp Sci Valparaiso Chile

ISBN: (纸本)9783319107622;9783319107615

Metaheuristic design is an incremental and difficult task. It is usually iterative and requires several evaluations of the code to obtain an algorithm with good performance. In this work, we analyse the design of metaheuristics by detecting components which are strictly necessary to obtain a good performance (in term of solutions quality). We use a collective strategy where the information generated by a tuner is used to detect the components usefulness. We evaluate this strategy with two well-known tuners EVOCA and I-RACE to analyse which one is more suitable and provides better results to make this components detection. The goal is to help the designer either to evaluate during the design process different options of the code or to simplify her/his final code without a loss in the quality of the solutions.

关键词： automated algorithm tuning automated algorithm configuration metaheuristics

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Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms

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SWARM INTELLIGENCE 2012年第1期6卷 49-75页

作者： Yuan, Zhi de Oca, Marco A. Montes Birattari, Mauro Stuetzle, Thomas Univ Libre Bruxelles CoDE IRIDIA Brussels Belgium

The performance of optimization algorithms, including those based on swarm intelligence, depends on the values assigned to their parameters. To obtain high performance, these parameters must be fine-tuned. Since many parameters can take real values or integer values from a large domain, it is often possible to treat the tuning problem as a continuous optimization problem. In this article, we study the performance of a number of prominent continuous optimization algorithms for parameter tuning using various case studies from the swarm intelligence literature. The continuous optimization algorithms that we study are enhanced to handle the stochastic nature of the tuning problem. In particular, we introduce a new post-selection mechanism that uses F-Race in the final phase of the tuning process to select the best among elite parameter configurations. We also examine the parameter space of the swarm intelligence algorithms that we consider in our study, and we show that by fine-tuning their parameters one can obtain substantial improvements over default configurations.

关键词： automated algorithm configuration Parameter tuning Continuous optimization algorithm Swarm intelligence F-Race

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