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

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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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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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FIT calculator: a multi-risk prediction framework for medical outcomes using cardiorespiratory fitness data

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SCIENTIFIC REPORTS 2024年第1期14卷 1-10页

作者： Elshawi, Radwa Sakr, Sherif Al-Mallah, Mouaz H. Keteyian, Steven J. Brawner, Clinton A. Ehrman, Jonathan K. Univ Tartu Inst Comp Sci Tartu Estonia Houston Methodist DeBakey Heart & Vasc Ctr Houston TX USA Henry Ford Hosp Div Cardiovasc Med 6525 Second Ave Detroit MI 48202 USA

Accurately predicting patients' risk for specific medical outcomes is paramount for effective healthcare management and personalized medicine. While a substantial body of literature addresses the prediction of diverse medical conditions, existing models predominantly focus on singular outcomes, limiting their scope to one disease at a time. However, clinical reality often entails patients concurrently facing multiple health risks across various medical domains. In response to this gap, our study proposes a novel multi-risk framework adept at simultaneous risk prediction for multiple clinical outcomes, including diabetes, mortality, and hypertension. Leveraging a concise set of features extracted from patients' cardiorespiratory fitness data, our framework minimizes computational complexity while maximizing predictive accuracy. Moreover, we integrate a state-of-the-art instance-based interpretability technique into our framework, providing users with comprehensive explanations for each prediction. These explanations afford medical practitioners invaluable insights into the primary health factors influencing individual predictions, fostering greater trust and utility in the underlying prediction models. Our approach thus stands to significantly enhance healthcare decision-making processes, facilitating more targeted interventions and improving patient outcomes in clinical practice. Our prediction framework utilizes an automated machine learning framework, Auto-Weka, to optimize machine learning models and hyper-parameter configurations for the simultaneous prediction of three medical outcomes: diabetes, mortality, and hypertension. Additionally, we employ a local interpretability technique to elucidate predictions generated by our framework. These explanations manifest visually, highlighting key attributes contributing to each instance's prediction for enhanced interpretability. Using automated machine learning techniques, the models simultaneously predict hypertensio

关键词： Prediction model Classification techniques Interpretability automatic algorithm selection Hyperparameter optimization

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A Hyperheuristic and Reinforcement Learning Guided Meta-heuristic algorithm Recommendation 27

A Hyperheuristic and Reinforcement Learning Guided Meta-heur...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Zhu, Ningning Zhao, Fuqing Cao, Jie Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China

ISBN: (纸本)9798350349184;9798350349191

automatic selection of the most appropriate algorithms for complex optimization problems has emerged as a cutting-edge trend in artificial intelligence. This approach circumvents the interpretability challenges posed through trial and error. A hyperheuristic and reinforcement learning-guided meta-heuristic algorithm recommendation (HHRL-MAR) is proposed to facilitate the adaptive selection of a diverse array of meta-heuristic algorithms tailored to the unique characteristics of various problems in this paper. To this end, four meta-heuristics with distinct advantages are integrated to form the action space within the reinforcement learning, serving as the low-level heuristic for hyperheuristic. The incorporated reward mechanism based on the real-time state of the population enhances both the flexibility and accuracy of the algorithm. Three selection strategies in light of simulated annealing and epsilon - greedy are designed to avoid premature convergence associated with a singular selection approach. The experimental results show the efficacy of HHRL-MAR for large-scale complex continuous optimization in terms of accuracy, stability, and convergence speed.

关键词： reinforcement learning hyperheuristic algorithm automatic algorithm selection meta-heuristic algorithm population activation procedure

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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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N ways to simulate short-range particle systems: Automated algorithm selection with the node-level library AutoPas

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COMPUTER PHYSICS COMMUNICATIONS 2022年 273卷 108262-108262页

作者： Gratl, Fabio Alexander Seckler, Steffen Bungartz, Hans-Joachim Neumann, Philipp Tech Univ Munich Chair Sci Comp Comp Sci Munich Germany Helmut Schmidt Univ Chair High Performance Comp Hamburg Germany

AutoPas is an open-source C++ library delivering optimal node-level performance by providing the ideal algorithmic configuration for an arbitrary scenario in a given short-range particle simulation. It acts as a black-box container, internally implementing an extensive set of algorithms, parallelization strategies, and optimizations that are combined dynamically according to the state of the simulation via auto-tuning. This paper gives an overview of the high-level user perspective, as well as the internal view, covering the implemented techniques and features. The library is showcased by incorporating it into the codes LAMMPS and ls1 mardyn, and by investigating various applications. We further outline node-level shared-memory performance and scalability of our auto-tuning software which is comparable to LAMMPS. (C) 2021 Elsevier B.V. All rights reserved.

关键词： HPC N-body simulation Molecular dynamics Auto-tuning automatic algorithm selection Dynamic 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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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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AutoPas: Auto-Tuning for Particle Simulations 33

AutoPas: Auto-Tuning for Particle Simulations

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33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Gratl, Fabio Seckler, Steffen Tchipev, Nikola Bungartz, Hans-Joachim Neumann, Philipp Tech Univ Munich Chair Sci Comp Comp Sci Munich Germany Univ Hamburg Sci Comp Hamburg Germany

ISBN: (纸本)9781538655559

The C++ library AutoPas aims at delivering optimal node-level performance for particle simulations. This paper describes the internally implemented algorithms, and how the library uses auto-tuning to dynamically select their optimal combination at run-time. Results are presented, which show that all available algorithms and configuration options have their specific advantages. To demonstrate the library's capabilities in relevant application settings, it has been integrated into the software package ls1 mardyn. An example of a realistic molecular dynamics simulation from thermodynamics is shown in which AutoPas detects a change in the best possible algorithm configuration. It adapts the simulation algorithm accordingly, sustaining optimal performance without additional user input.

关键词： HPC Molecular Dynamics Auto-Tuning automatic algorithm selection Dynamic Tuning

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