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An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2014年第3期233卷 511-528页

作者： Messelis, Tommy De Causmaecker, Patrick CODeS Res Grp B-8500 Kortrijk Belgium

This paper investigates the construction of an automatic algorithm selection tool for the multi-mode resource-constrained project scheduling problem (MRCPSP). The research described relies on the notion of empirical hardness models. These models map problem instance features onto the performance of an algorithm. Using such models, the performance of a set of algorithms can be predicted. Based on these predictions, one can automatically select the algorithm that is expected to perform best given the available computing resources. The idea is to combine different algorithms in a super-algorithm that performs better than any of the components individually. We apply this strategy to the classic problem of project scheduling with multiple execution modes. We show that we can indeed significantly improve on the performance of state-of-the-art algorithms when evaluated on a set of unseen instances. This becomes important when lots of instances have to be solved consecutively. Many state-of-the-art algorithms perform very well on a majority of benchmark instances, while performing worse on a smaller set of instances. The performance of one algorithm can be very different on a set of instances while another algorithm sees no difference in performance at all. Knowing in advance, without using scarce computational resources, which algorithm to run on a certain problem instance, can significantly improve the total overall performance. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Decision support systems Combinatorial optimisation Performance prediction Project scheduling algorithm portfolio automatic algorithm selection

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automatic algorithm selection in Computational Software Using Machine Learning

Automatic Algorithm Selection in Computational Software Usin...

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IEEE International Conference on Machine Learning and Applications

作者： Matthew C. Simpson Qing Yi Jugal Kalita NC State University Raleigh NC 27695 University of Colorado Colorado Springs CO 80918

ISBN: (纸本)9781509061686

Computational software programs, such as Maple and Mathematica, heavily rely on superfunctions and meta-algorithms to select the optimal algorithm for a given task. These meta-algorithms may require intensive mathematical proof to formulate, incur large computational overhead, or fail to consistently select the best algorithm. Machine learning demonstrates a promising alternative for automatic algorithm selection by easing the design process and overhead while also attaining high accuracy in selection. In a case study on the resultant superfunction, a trained neural network is able to select the best algorithm out of the four available 86% of the time in Maple and 78% of the time in Mathematica. When used as a replacement for pre-existing meta-algorithms, the neural network brings about a 68% runtime improvement in Maple and 49% improvement in Mathematica. Random forests, k-nearest neighbors, and both linear and RBF kernel SVMs are also compared to the neural network model, the latter of which offers the best performance out of the tested machine learning methods.

关键词： automatic algorithm selection Computational Software Machine Learning

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automaticAI-A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115225-115225页

作者： Czako, Zoltan Sebestyen, Gheorghe Hangan, Anca Tech Univ Cluj Napoca Dept Comp Sci Cluj Napoca Romania

Recently, more and more real life problems are solved using artificial intelligence (AI) algorithms. One of the biggest challenges when working with AI is the selection and tuning of the best algorithm for solving the problem. The results generated by a given AI algorithm heavily depend on the way in which its hyperparameters are set. In most cases the process of algorithm selection and tuning is a manual, time consuming process in which the developer, based on experience and intuition tries to find the best solution from quality and execution time perspective. In this paper we present a method for solving the problem of AI algorithm selection and tuning, without human intervention, in a fully automated way. The method is a hybrid approach, a combination between particle swarm optimization and simulated annealing. We compare our approach with other similar tools like Auto-sklearn or Hyperopt-sklearn. We demonstrate the time efficiency and high accuracy of this method with some experiments on some known datasets. The paper also presents a platform for AI processing that include a set of procedures and services necessary in case of automatic processing of big datasets as well as the method for AI algorithm selection and tuning. This platform is useful for researchers and developers in an incipient phase of application development, when the best solution must be decided;it is also useful for specialists in different domains (physics, industry, economy) with less experience in using AI algorithms, but which has to process huge amount of data in an automated way.

关键词： Particle swarm optimization Simulated annealing automatic algorithm selection automatic hyperparameter tuning

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MLtool: A Tool to Automate the Construction, Evaluation, and selection of Machine Learning Models

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IEEE LATIN AMERICA TRANSACTIONS 2019年第7期17卷 1163-1170页

作者： Mendes, C. de Barcelos, A. Rigo, S. Univ Vale Rio Sinos Unisinos Sao Leopoldo RS Brazil

The use of Machine Learning has intensified in recent years, gaining notoriety in the most diverse applications. Thus, there is a growing demand for professionals in this area, which has favored the entry of countless inexperienced users in the labor market. The path from the initial step to the use of algorithms and Machine Learning techniques in production environment takes considerable time, even from experts, due to the realization of interactive and manual tasks required for building predictive models that provide the desired results. In view of this, this work presents a tool to automate the construction, evaluation and selection of predictive models considering Machine Learning algorithms and parameters values more appropriate for each situation. The stages of feature selection, standardization, resampling, and training in the construction of models were considered in the tool. The proposed tool deal with the treatment of the problem of data unbalancing, as needed, as well as the execution control of the steps involved in the process of creating predictive models. The results obtained demonstrate that the order and choice of the steps and the values chosen for the algorithm parameters affect the final results of the generated models.

关键词： Machine Learning Model selection automatic algorithm selection Automated Machine Learning

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RECURSIVE automatic BIAS selection FOR CLASSIFIER CONSTRUCTION

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MACHINE LEARNING 1995年第1-2期20卷 63-94页

作者： BRODLEY, CE School of Electrical and Computer Engineering Purdue University West Lafayette IN 47907.brodley@ecn.purdue.edu

The results of empirical comparisons DE existing learning algorithms illustrate that each algorithm has a selective superiority;each is best for some but not ail tasks. Given a data set, it is often not clear beforehand which algorithm will yield the best performance. In this article we present an approach that uses characteristics of the given data set, in the form of feedback from the learning process, to guide a search for a tree-structured hybrid classifier. Heuristic knowledge about the characteristics that indicate one bias is better than another is encoded in the rule base of the Model Class selection (MCS) system. The approach does not assume that the entire instance space is best learned using a single representation language;for some data sets, choosing to form a hybrid classifier is a better bias, and MCS has the ability to determine these cases. The results of an empirical evaluation illustrate that MCS achieves classification accuracies equal to or higher than the best of its primitive learning components for each data set, demonstrating that the heuristic rules effectively select an appropriate learning bias.

关键词： INDUCTIVE BIAS HYBRID CLASSIFIERS automatic algorithm selection DECISION TREES LEARNING FROM EXAMPLES

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A review of automatic selection methods for machine learning algorithms and hyper-parameter values

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2016年第1期5卷 1-16页

作者： Luo, Gang Univ Utah Dept Biomed Informat Suite 140421 Wakara Way Salt Lake City UT 84108 USA

Machine learning studies automatic algorithms that improve themselves through experience. It is widely used for analyzing and extracting value from large biomedical data sets, or "big biomedical data,'' advancing biomedical research, and improving healthcare. Before a machine learning model is trained, the user of a machine learning software tool typically must manually select a machine learning algorithm and set one or more model parameters termed hyper-parameters. The algorithm and hyper-parameter values used can greatly impact the resulting model's performance, but their selection requires special expertise as well as many labor-intensive manual iterations. To make machine learning accessible to layman users with limited computing expertise, computer science researchers have proposed various automatic selection methods for algorithms and/or hyper-parameter values for a given supervised machine learning problem. This paper reviews these methods, identifies several of their limitations in the big biomedical data environment, and provides preliminary thoughts on how to address these limitations. These findings establish a foundation for future research on automatically selecting algorithms and hyper-parameter values for analyzing big biomedical data.

关键词： Machine learning Big biomedical data automatic algorithm selection automatic hyper-parameter value selection

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automatic Surrogate Modelling Technique selection based on Features of Optimization Problems 19

Automatic Surrogate Modelling Technique Selection based on F...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Saini, Bhupinder Singh Lopez-Ibanez, Manuel Miettinen, Kaisa Univ Jyvaskyla Fac Informat Technol POB 35 Agora FI-40014 Jyvaskyla Finland Univ Manchester Alliance Manchester Business Sch Manchester Lancs England

ISBN: (纸本)9781450367486

A typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the literature, there is no standard procedure that will select the best technique for a given problem. In this work, we propose the automatic selection of a surrogate modelling technique based on exploratory landscape features of the optimization problem that underlies the given dataset. The overall idea is to learn offline from a large pool of benchmark problems, on which we can evaluate a large number of surrogate modelling techniques. When given a new dataset, features are used to select the most appropriate surrogate modelling technique. The preliminary experiments reported here suggest that the proposed automatic selector is able to identify high-accuracy surrogate models as long as an appropriate classifier is used for selection.

关键词： surrogate modelling automatic algorithm selection exploratory landscape analysis

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Applying algorithm selection – a Case Study for the Generalised Assignment Problem

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Electronic Notes in Discrete Mathematics 2018年 69卷 205-212页

作者： Degroote, Hans González-Velarde, José Luis De Causmaecker, Patrick Department of Computer Science CODeS & Imec KU Leuven Belgium Tecnologico de Monterrey Monterrey Mexico

Many combinatorial optimisation problems are NP-Hard. Yet in practice high quality solutions are often obtained by (meta)heuristics. These work well in some cases, but not in others, indicating a potential for algorithm selection. In this extended abstract is discussed how to apply algorithm selection to a combinatorial optimisation problem and which challenges arise when doing so. The generalised assignment problem is used as case study. © 2018 Elsevier B.V.

关键词： automatic algorithm selection Combinatorial Optimisation Generalised Assignment Problem Generalized Assignment Problem Meta-learning

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automatic selection of Binarization Method for Robust OCR

Automatic Selection of Binarization Method for Robust OCR

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12th International Conference on Document Analysis and Recognition (ICDAR)

作者： Chattopadhyay, T. Reddy, V. Ramu Garain, Utpal Tata Consultancy Serv Innovat Lab Kolkata India Indian Stat Inst CVPR Unit Kolkata India

ISBN: (纸本)9780769549993

Many algorithms are now available for doing the same task (e.g. binarization, page segmentation, character recognition, etc.) in document image analysis (DIA) and choosing a particular algorithm(s) for a particular task is often a non-trivial problem. This paper proposes a model for automatically selecting the correct algorithm(s) for a given problem. Binarization has been taken a reference to illustrate the proposed approach. Several previously unexplored issues are addressed in this work. For example, only one method may not be good for the binarization of an entire document whereas a particular method may produce desired result for a particular region. Therefore, for a given document image, our model selects a set of one or more binarization techniques suitable for different regions of the document. This selection is completely automatic and guided by the machine learning approaches. Formulation of a completely automatic way for generating the annotated data for training the learning algorithms is also a novel contribution of this work. Evaluation of the approach is done using ICDAR 2003 Robust Reading data set and results highlight the potential of the proposed approach for automatic selection of correct DIA algorithm(s) from a set of several alternatives.

关键词： automatic algorithm selection Binarization techniques Machine learning automatic generation of training data Evaluation

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Comparative Research of Hyper-Parameters Mathematical Optimization algorithms for automatic Machine Learning in New Generation Mobile Network

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MOBILE NETWORKS & APPLICATIONS 2022年第3期27卷 928-935页

作者： Zhang, Xiaohang Li, Yuqi Li, Zhengren Beijing Univ Posts & Telecommun Sch Econ & Management Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Modern Post Beijing 100876 Peoples R China

Under the configuration of the new generation communication network, the algorithm based on machine learning has been widely used in network optimization and mobile user behavior prediction. Therefore, the optimization method with hyper-parameters will have a huge development space in the field of mobile communication network. However, for non-professionals, the bottleneck that restricts the further development and application of the whole machine learning is the selection of suitable machine learning algorithm and the determination of suitable algorithm hyper-parameters. Researchers have proposed to use automatic machine learning algorithm to solve this remarkable problem. This article forms a technical manual that can be easily searched by researchers with summarizing related hyper-parameter optimization methods and proposing the corresponding algorithm framework. Moreover, through the comparison of related optimization methods, we highlight the characteristics and deficiencies of related algorithms in the new generation of mobile networks, and put forward suggestions for future improvement.

关键词： Machine learning automatic algorithm selection Hyper-parameter optimization 5G New generation mobile network Information technology

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