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SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-9页

作者： Lindauer, Marius Eggensperger, Katharina Feurer, Matthias Biedenkapp, Andre Deng, Difan Benjamins, Carolin Ruhkopf, Tim Sass, Rene Hutter, Frank Leibniz Univ Hannover Hannover Germany Univ Freiburg Freiburg Germany Bosch Ctr Artificial Intelligence Renningen Germany

algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at https://***/automl/SMAC3.

关键词： Bayesian Optimization Hyperparameter Optimization Multi-Fidelity Optimization Automated Machine Learning algorithm configuration

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Few-Shots Parallel algorithm Portfolio Construction via Co-Evolution

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2021年第3期25卷 595-607页

作者： Tang, Ke Liu, Shengcai Yang, Peng Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Key Lab Brain Inspired Intelligent Comp Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China

Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast data. In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of "training" problem instances, which is often referred to as PAP construction. However, compared to the traditional machine learning, PAP construction often suffers from the lack of training instances, and the obtained PAPs may fail to generalize well. This article proposes a novel competitive co-evolution scheme, named co-evolution of parameterized search (CEPS), as a remedy to this challenge. By co-evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable PAPs with few training instances. The advantage of CEPS in improving generalization is analytically shown in this article. Two concrete algorithms, namely, CEPS-TSP and CEPS-VRPSPDTW, are presented for the traveling salesman problem (TSP) and the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW), respectively. The experimental results show that CEPS has led to better generalization, and even managed to find new best-known solutions for some instances.

关键词： Training Portfolios Tuning Statistics Sociology Parallel algorithms Machine learning algorithms algorithm configuration automatic parameter tuning co-evolution parallel algorithm portfolios (PAPs) vehicle routing problems

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RECIFE-MILP: An Effective MILP-Based Heuristic for the Real-Time Railway Traffic Management Problem

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2015年第5期16卷 2609-2619页

作者： Pellegrini, Paola Marliere, Gregory Pesenti, Raffaele Rodriguez, Joaquin Univ Lille Nord France F-59000 Lille France LEOST COSYS IFSTTAR F-59650 Villeneuve Dascq France ESTAS COSYS IFSTTAR F-59650 Villeneuve Dascq France Ca Foscari Univ I-30121 Venice Italy

The real-time railway traffic management problem consists of selecting appropriate train routes and schedules for minimizing the propagation of delay in case of traffic perturbation. In this paper, we tackle this problem by introducing RECIFE-MILP, a heuristic algorithm based on a mixed-integer linear programming model. RECIFE-MILP uses a model that extends one we previously proposed by including additional elements characterizing railway reality. In addition, it implements performance boosting methods selected among several ones through an algorithm configuration tool. We present a thorough experimental analysis that shows that the performances of RECIFE-MILP are better than the ones of the currently implemented traffic management strategy. RECIFE-MILP often finds the optimal solution to instances within the short computation time available in real-time applications. Moreover, RECIFE-MILP is robust to its configuration if an appropriate selection of the combination of boosting methods is performed.

关键词： Real-time railway traffic management problem mixed-integer linear programming algorithm configuration performance boosting

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ML-Plan: Automated machine learning via hierarchical planning

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MACHINE LEARNING 2018年第8-10期107卷 1495-1515页

作者： Mohr, Felix Wever, Marcel Huellermeier, Eyke Paderborn Univ Warburger Str 100 D-33098 Paderborn Germany

Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.

关键词： Automated machine learning Automated planning algorithm selection algorithm configuration Heuristic search

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Automatic model selection for high-dimensional survival analysis

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2015年第1期85卷 62-76页

作者： Lang, M. Kotthaus, H. Marwedel, P. Weihs, C. Rahnenfuehrer, J. Bischl, B. TU Dortmund Univ Dept Stat D-44227 Dortmund Germany TU Dortmund Univ Dept Comp Sci 12 D-44227 Dortmund Germany

Many different models for the analysis of high-dimensional survival data have been developed over the past years. While some of the models and implementations come with an internal parameter tuning automatism, others require the user to accurately adjust defaults, which often feels like a guessing game. Exhaustively trying out all model and parameter combinations will quickly become tedious or infeasible in computationally intensive settings, even if parallelization is employed. Therefore, we propose to use modern algorithm configuration techniques, e.g. iterated F-racing, to efficiently move through the model hypothesis space and to simultaneously configure algorithm classes and their respective hyperparameters. In our application we study four lung cancer microarray data sets. For these we configure a predictor based on five survival analysis algorithms in combination with eight feature selection filters. We parallelize the optimization and all comparison experiments with the BatchJobs and BatchExperiments R packages.

关键词： model selection feature selection algorithm configuration survival analysis machine learning high-dimensional data racing parameter tuning

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Reuse-centric k-means configuration

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INFORMATION SYSTEMS 2021年 100卷 101787-101787页

作者： Zhang, Lijun Guan, Hui Ding, Yufei Shen, Xipeng Krim, Hamid Univ Massachusetts Amherst MA 01002 USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA North Carolina State Univ Raleigh NC 27606 USA

K-means configuration is to find a configuration of k-means (e.g., the number of clusters, feature sets) that maximize some objectives. It is a time-consuming process due to the iterative nature of k-means. This paper proposes reuse-centric k-means configuration to accelerate k-means configuration. It is based on the observation that the explorations of different configurations share lots of common or similar computations. Effectively reusing the computations from prior trials of different configurations could largely shorten the configuration time. To materialize the idea, the paper presents a set of novel techniques, including reuse-based filtering, center reuse, and a two-phase design to capitalize on the reuse opportunities on three levels: validation, number of clusters, and feature sets. Experiments on k-means-based data classification tasks show that reuse-centric k-means configuration can speed up a heuristic search-based configuration process by a factor of 5.8, and a uniform search-based attainment of classification error surfaces by a factor of 9.1. The paper meanwhile provides some important insights on how to effectively apply the acceleration techniques to tap into a full potential. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： K-means algorithm configuration Computation reuse

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Tradeoffs in the empirical evaluation of competing algorithm designs

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2010年第1-2期60卷 65-89页

作者： Hutter, Frank Hoos, Holger H. Leyton-Brown, Kevin Univ British Columbia Vancouver BC V6T 1Z4 Canada

We propose an empirical analysis approach for characterizing tradeoffs between different methods for comparing a set of competing algorithm designs. Our approach can provide insight into performance variation both across candidate algorithms and across instances. It can also identify the best tradeoff between evaluating a larger number of candidate algorithm designs, performing these evaluations on a larger number of problem instances, and allocating more time to each algorithm run. We applied our approach to a study of the rich algorithm design spaces offered by three highly-parameterized, state-of-the-art algorithms for satisfiability and mixed integer programming, considering six different distributions of problem instances. We demonstrate that the resulting algorithm design scenarios differ in many ways, with important consequences for both automatic and manual algorithm design. We expect that both our methods and our findings will lead to tangible improvements in algorithm design methods.

关键词： algorithm design Empirical analysis algorithm configuration Parameter optimization

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ACVIZ: A tool for the visual analysis of the configuration of algorithms with irace

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OPERATIONS RESEARCH PERSPECTIVES 2021年 8卷

作者： de Souza, Marcelo Ritt, Marcus Lopez-Ibanez, Manuel Perez Caceres, Leslie Santa Catarina State Univ Florianopolis SC Brazil Univ Fed Rio Grande do Sul Porto Alegre RS Brazil Univ Malaga Malaga Spain Pontificia Univ Catolica Valparaiso Valparaiso Chile

This paper introduces acviz, a tool that helps to analyze the automatic configuration of algorithms with irace. It provides a visual representation of the configuration process, allowing users to extract useful information, e.g. how the configurations evolve over time. When test data is available, acviz also shows the performance of each configuration on the test instances. Using this visualization, users can analyze and compare the quality of the resulting configurations and observe the performance differences on training and test instances.

关键词： algorithm configuration Parameter tuning irace

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Online Selection of CMA-ES Variants

Online Selection of CMA-ES Variants

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vermetten, Diederick van Rijn, Sander Back, Thomas Doerr, Carola Leiden Inst Adv Comp Sci Leiden Netherlands Sorbonne Univ LIP6 Paris France CNRS Paris France

ISBN: (纸本)9781450361118

In the field of evolutionary computation, one of the most challenging topics is algorithm selection. Knowing which heuristics to use for which optimization problem is key to obtaining high-quality solutions. We aim to extend this research topic by taking a first step towards a selection method for adaptive CMA-ES algorithms. We build upon the theoretical work done by van Rijn et al. [PPSN'18], in which the potential of switching between different CMA-ES variants was quantified in the context of a modular CMA-ES framework. We demonstrate in this work that their proposed approach is not very reliable, in that implementing the suggested adaptive configurations does not yield the predicted performance gains. We propose a revised approach, which results in a more robust fit between predicted and actual performance. The adaptive CMA-ES approach obtains performance gains on 18 out of 24 tested functions of the BBOB benchmark, with stable advantages of up to 23%. An analysis of module activation indicates which modules are most crucial for the different phases of optimizing each of the 24 benchmark problems. The module activation also suggests that additional gains are possible when including the (B)IPOP modules, which we have excluded for this present work.

关键词： Evolution Strategies algorithm configuration algorithm Selection

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algorithm Selection across algorithm Configurators A Case Study on Multi-objective Optimization

Algorithm Selection across Algorithm Configurators A Case St...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Misir, Mustafa Duke Kunshan Univ Div Nat & Appl Sci Kunshan Peoples R China

ISBN: (纸本)9781665487689

The present work utilizes algorithm Selection for automatically specifying the parameter tuning method for a given tuning task. The idea of parameter tuning is motivated by the premature algorithm designs and their sub-optimal or poor parameter value choices. The decisions both on the designs and the parameter values are largely given based on the experiences of the developers or the target problem domain experts after a limited number of trials. While the existing tuning approaches tend to offer improvements over the default parameter values for varying algorithms, they can be computationally expensive. Additionally, there is no a single, ultimate parameter tuning strategy. These facts suggest to choose the most effective tuning algorithm for a specific scenario. This study utilizes an existing algorithm Selection system to address this problem. The idea is to allocate potentially the most effective tuning method for a given task instead of relying on a single tuner. On that note, a group of well-known parameter configuration approaches are accommodated as the candidate methods to tune NSGA-II for solving a suite of multi-objective optimization benchmarks, referencing a recent article. The computational analysis revealed that algorithm Selection outperforms those constituent parameter tuning methods when each is used as a standalone manner. Additionally, the dis/-similarity analysis carried on the problem instances / benchmarks give hints on the diversity level of the benchmarks. Furthermore, a similar inspection reported on the parameter tuning procedures show the behavioural resemblance between them.

关键词： algorithm selection algorithm configuration parameter tuning multi-objective optimization

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