146 results
Search Results
2. CNN-HT: A Two-Stage Algorithm Selection Framework.
- Author
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Xu, Siyi, Liu, Wenwen, Wu, Chengpei, and Li, Junli
- Subjects
CONVOLUTIONAL neural networks ,NAIVE Bayes classification ,FEATURE selection ,SUPPORT vector machines ,ALGORITHMS ,RANDOM forest algorithms - Abstract
The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the statistical analysis of the performance metric of algorithms that address various problem categories. The two-stage approach can adapt to different algorithm combinations without the need to retrain the entire model, and modifications can be made in the second stage only, which is an improvement of one-stage approaches. To provide a more general structure for the classification model, we adopt Exploratory Landscape Analysis (ELA) features of the problem as input and utilize feature selection techniques to reduce the redundant ones. In problem classification, the average accuracy of classifying problems using CNN is 96%, which demonstrates the advantages of CNN compared to Random Forest and Support Vector Machines. After feature selection, the accuracy increases to 98.8%, further improving the classification performance while reducing the computational cost. This demonstrates the effectiveness of the first stage of the CNN-HT method, which provides a basis for algorithm selection. In the experiments, CNN-HT shows the advantages of the second stage algorithm as well as good performance with better average rankings in different algorithm combinations compared to the individual algorithms and another algorithm combination approach. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. Algorithm selection using edge ML and case-based reasoning.
- Author
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Ali, Rahman, Zada, Muhammad Sadiq Hassan, Khatak, Asad Masood, and Hussain, Jamil
- Subjects
CASE-based reasoning ,DECISION trees ,CLASSIFICATION algorithms ,MACHINE learning ,ALGORITHMS ,DATA mining ,EMPIRICAL research ,FEATURE extraction - Abstract
In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data scarcity mitigation through incremental learning, and an open-source CBR module, reshaping research paradigms. The CBR module, trained on 100 datasets and tested with 52 datasets using 9 decision tree algorithms, achieved an accuracy of 94% for correct classifier recommendations within the top k=3 algorithms, making it highly suitable for practical classification applications. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
4. Automated model selection for multivariate anomaly detection in manufacturing systems
- Author
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Engbers, Hendrik and Freitag, Michael
- Published
- 2024
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- View/download PDF
5. Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning.
- Author
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Schweitzer, Felicia, Bitsch, Günter, and Louw, Louis
- Subjects
AUTOMATED guided vehicle systems ,AUTOMOBILE industry ,MACHINE learning ,PRODUCTION scheduling ,AUTOMATED planning & scheduling ,SATISFIABILITY (Computer science) - Abstract
Featured Application: The artifact developed in this article is applicable to AGV and production planning optimization problems that align with the principles of the job-shop scheduling problem (JSSP) and the flexible job-shop scheduling problem (FJSSP). Artificial intelligence is considered to be a significant technology for driving the future evolution of smart manufacturing environments. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems due to their potential to improve internal logistics by increasing production flexibility. Thereby, the productivity of the entire system relies on the quality of the schedule, which can achieve production cost savings by minimizing delays and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. Therefore, this paper aimed to analyze the scheduling problem classes of AGVs by applying design science research to develop an algorithm selection approach. The designed artifact addressed a catalogue of characteristics that used several machine learning algorithms to find the optimal solution strategy for the intended scheduling problem. The contribution of this paper is the creation of an algorithm selection method that automatically selects a scheduling algorithm, depending on the problem class and the algorithm space. In this way, production efficiency can be increased by dynamically adapting the AGV schedules. A computational study with benchmark literature instances unveiled the successful implementation of constraint programming solvers for solving JSSP and FJSSP scheduling problems and machine learning algorithms for predicting the most promising solver. The performance of the solvers strongly depended on the given problem class and the problem instance. Consequently, the overall production performance increased by selecting the algorithms per instance. A field experiment in the learning factory at Reutlingen University enabled the validation of the approach within a running production scenario. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
6. Algorithm selection for SMT: MachSMT: Machine Learning Driven Algorithm Selection for SMT Solvers.
- Author
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Scott, Joseph, Niemetz, Aina, Preiner, Mathias, Nejati, Saeed, and Ganesh, Vijay
- Subjects
MACHINE learning ,FLOATING-point arithmetic ,SATISFIABILITY (Computer science) - Abstract
This paper presents MachSMT, an algorithm selection tool for Satisfiability Modulo Theories (SMT) solvers. MachSMT supports the entirety of the SMT-LIB language and standardized SMT-LIB theories, and is easy to extend with support for new theories. MachSMT deploys machine learning methods to construct both empirical hardness models and pairwise ranking comparators over state-of-the-art SMT solvers. Given an input formula in SMT-LIB format, MachSMT leverages these learnt models to output a ranking of solvers based on predicted runtimes. We provide an extensive empirical evaluation of MachSMT to demonstrate the efficiency and efficacy of MachSMT over three broad usage scenarios on theories and theory combinations of practical relevance (e.g., bit-vectors, (non)linear integer and real arithmetic, arrays, and floating-point arithmetic). First, we deploy MachSMT on state-of-the-art solvers in SMT-COMP 2019 and 2020. We observe MachSMT frequently improves on the best performing solvers in the competition, winning 57 divisions outright, with up to a 99.4 % improvement in PAR-2 score. Second, we evaluate MachSMT to select configurations from a single underlying solver. We observe that MachSMT solves 898 more benchmarks and up to a 93.4 % improvement in PAR-2 score across 23 configurations of the SMT solver cvc5. Last, we evaluate MachSMT on domain-specific problems, namely network verification with simple domain-specific features, and observe an improvement of 77.3 % in PAR-2 score. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
7. Autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithms.
- Author
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Garouani, Moncef, Ahmad, Adeel, Bouneffa, Mourad, and Hamlich, Mohamed
- Subjects
MACHINE learning ,FEATURE extraction ,STATISTICS ,INFORMATION theory ,ARTIFICIAL intelligence ,ALGORITHMS - Abstract
The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the configuration of its hyperparameters is among the most complicated tasks while applying ML to new problems. It necessitates well awareness and knowledge of ML algorithms. The algorithm selection problem (ASP) is defined as the process of identifying the algorithm (s) that can deliver top performance for a particular problem, task, and evaluation measure. In this context, meta-learning is one of the approaches to achieve this objective by using prior learning experiences to assist the learning process on unseen problems and tasks. As a data-driven approach, appropriate data characterization is of vital importance for the meta-learning. Nonetheless, the recent literature witness a variety of data characterization techniques including simple, statistical and information theory based measures. However, their quality still needs to be improved. In this paper, a new Autoencoder-kNN (AeKNN) based meta-model with built-in latent features extraction is proposed. The approach is aimed to extract new characterizations of the data, with lower dimensionality but more significant and meaningful features. AeKNN internally uses a deep autoencoder as a latent features extractor from a set of existing meta-features induced from the dataset. From this new features vectors the computed distances are more significant, thus providing a way to accurately recommending top-performing pipelines for previously unseen datasets. In an application on a large-scale hyperparameters optimization task for 400 real world datasets with varying schemas as a meta-learning task, we show that AeKNN offers considerable improvements of the classical kNN as well as traditional meta-models in terms of performance. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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8. Survey on Meta-Learning Research of Algorithm Selection.
- Author
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LI Gengsong, LIU Yi, QIN Wei, LI Hongmei, ZHENG Qibin, SONG Mingwu, and REN Xiaoguang
- Subjects
MACHINE learning ,CLASSIFICATION algorithms ,METAHEURISTIC algorithms ,ARTIFICIAL intelligence ,PROBLEM solving - Abstract
With the rapid development of artificial intelligence, the selection of algorithms that meet application requirements from feasible algorithms has become a critical problem to be solved urgently in various fields, that is, the algorithm selection problem. The approach based on meta-learning is an important way to solve the algorithm selection problem, which is widely applied in algorithm selection research and achieves good results. The approach selects appropriate algorithms by constructing the mapping model from problem features to candidate algorithms performance, mainly including the steps of extracting meta-features, calculating candidate algorithms performance, constructing meta-dataset and training meta-model, etc. Firstly, this paper expounds the concept and framework of algorithm selection based on meta-learning, and reviews related surveys. Secondly, it summarizes the research progress from three aspects: meta-features, meta-learners and meta-model performance measures, introduces typical methods and compares the advantages, disadvantages and application scope of different types of methods. Then, it outlines the application of algorithm selection based on meta-learning in different learning tasks. Next, it utilizes 140 classification datasets, 9 candidate classification algorithms and 5 performance indicators to conduct algorithm selection experiments to compare the performance of different algorithm selection methods. Finally, it analyzes the current challenges and problems, and discusses future development directions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
9. A Decision-Making Tool for Algorithm Selection Based on a Fuzzy TOPSIS Approach to Solve Replenishment, Production and Distribution Planning Problems.
- Author
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Guzman, Eduardo, Andres, Beatriz, and Poler, Raul
- Subjects
DISTRIBUTION planning ,PRODUCTION planning ,TOPSIS method ,DECISION making ,FUZZY sets ,PROBLEM solving ,FUZZY neural networks - Abstract
A wide variety of methods and techniques with multiple characteristics are used in solving replenishment, production and distribution planning problems. Selecting a solution method (either a solver or an algorithm) when attempting to solve an optimization problem involves considerable difficulty. Identifying the best solution method among the many available ones is a complex activity that depends partly on human experts or a random trial-and-error procedure. This paper addresses the challenge of recommending a solution method for replenishment, production and distribution planning problems by proposing a decision-making tool for algorithm selection based on the fuzzy TOPSIS approach. This approach considers a collection of the different most commonly used solution methods in the literature, including distinct types of algorithms and solvers. To evaluate a solution method, 13 criteria were defined that all address several important dimensions when solving a planning problem, such as the computational difficulty, scheduling knowledge, mathematical knowledge, algorithm knowledge, mathematical modeling software knowledge and expected computational performance of the solution methods. An illustrative example is provided to demonstrate how planners apply the approach to select a solution method. A sensitivity analysis is also performed to examine the effect of decision maker biases on criteria ratings and how it may affect the final selection. The outcome of the approach provides planners with an effective and systematic decision support tool to follow the process of selecting a solution method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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10. Selecting an appropriate supervised machine learning algorithm for predictive maintenance.
- Author
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Ouadah, Abdelfettah, Zemmouchi-Ghomari, Leila, and Salhi, Nedjma
- Subjects
MACHINE learning ,SUPERVISED learning ,RANDOM forest algorithms ,DECISION trees ,CLASSIFICATION algorithms - Abstract
Predictive maintenance refers to predicting malfunctions using data from monitoring equipment and process performance measurements. Machine learning algorithms and techniques are often used to analyze equipment monitoring data. Machine learning is the process in which a computer can work more precisely by collecting and analyzing data. It is often the case that machine learning algorithms use supervised learning, in which labelled data is used to feed the algorithm. However, there are many supervised machine learning algorithms available. Therefore, choosing the best-supervised machine learning algorithm to resolve predictive maintenance issues is not trivial. This paper aims to increase the performance of predictive maintenance and achieve its goals by selecting the most suitable supervised machine learning algorithm. Based on the most commonly used criteria in research articles, we selected three supervised machine learning algorithms from a comparative study: Random forest, Decision tree and KNN. We then tested selected algorithms on data from real-world and simulation scenarios. Finally, we conducted the experiment based on vibration analysis and reliability evaluation. We noticed that Random forests and Decision trees obtained slightly the same performance. KNN is a better classification algorithm for extensive volumes of data; on the contrary, Random forest performs better in the case of small datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
11. Cognitive capabilities for the CAAI in cyber-physical production systems.
- Author
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Strohschein, Jan, Fischbach, Andreas, Bunte, Andreas, Faeskorn-Woyke, Heide, Moriz, Natalia, and Bartz-Beielstein, Thomas
- Subjects
CYBER physical systems ,ARTIFICIAL intelligence ,TELECOMMUNICATION ,BIG data ,MACHINE learning - Abstract
This paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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12. Algorithm selection for software validation based on graph kernels.
- Author
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Richter, Cedric, Hüllermeier, Eyke, Jakobs, Marie-Christine, and Wehrheim, Heike
- Subjects
SOFTWARE validation ,SOFTWARE verification ,REPRESENTATIONS of graphs ,ALGORITHMS ,KERNEL functions ,KERNEL operating systems - Abstract
Algorithm selection is the task of choosing an algorithm from a given set of candidate algorithms when faced with a particular problem instance. Algorithm selection via machine learning (ML) has recently been successfully applied for various problem classes, including computationally hard problems such as SAT. In this paper, we study algorithm selection for software validation, i.e., the task of choosing a software validation tool for a given validation instance. A validation instance consists of a program plus properties to be checked on it. The application of machine learning techniques to this task first of all requires an appropriate representation of software. To this end, we propose a dedicated kernel function, which compares two programs in terms of their similarity, thus making the algorithm selection task amenable to kernel-based machine learning methods. Our kernel operates on a graph representation of source code mixing elements of control-flow and program-dependence graphs with abstract syntax trees. Thus, given two such representations as input, the kernel function yields a real-valued score that can be interpreted as a degree of similarity. We experimentally evaluate our kernel in two learning scenarios, namely a classification and a ranking problem: (1) selecting between a verification and a testing tool for bug finding (i.e., property violation), and (2) ranking several verification tools, from presumably best to worst, for property proving. The evaluation, which is based on data sets from the annual software verification competition SV-COMP, demonstrates our kernel to generalize well and to achieve rather high prediction accuracy, both for the classification and the ranking task. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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13. Comprehensive Algorithm Portfolio Evaluation using Item Response Theory.
- Author
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Kandanaarachchi, Sevvandi and Smith-Miles, Kate
- Subjects
- *
ITEM response theory , *CLASSIFICATION algorithms , *MACHINE learning , *ALGORITHMS - Abstract
Item Response Theory (IRT) has been proposed within the field of Educational Psychometrics to assess student ability as well as test question difficulty and discrimination power. More recently, IRT has been applied to evaluate machine learning algorithm performance on a single classification dataset, where the student is now an algorithm, and the test question is an observation to be classified by the algorithm. In this paper we present a modified IRT-based framework for evaluating a portfolio of algorithms across a repository of datasets, while simultaneously eliciting a richer suite of characteristics - such as algorithm consistency and anomalousness - that describe important aspects of algorithm performance. These characteristics arise from a novel inversion and reinterpretation of the traditional IRT model without requiring additional dataset feature computations. We test this framework on algorithm portfolios for a wide range of applications, demonstrating the broad applicability of this method as an insightful algorithm evaluation tool. Furthermore, the explainable nature of IRT parameters yield an increased understanding of algorithm portfolios. [ABSTRACT FROM AUTHOR]
- Published
- 2023
14. EVALUATION OF COMPONENT ALGORITHMS IN AN ALGORITHM SELECTION APPROACH FOR SEMANTIC SEGMENTATION BASED ON HIGH-LEVEL INFORMATION FEEDBACK.
- Author
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Lukac, M., Abdiyeva, K., and Kameyama, M.
- Subjects
OBJECT recognition (Computer vision) ,ALGORITHMS ,FEATURE selection ,SEMANTICS ,COMPUTER vision ,IMAGE segmentation - Abstract
Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2016
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15. Instance space analysis for the car sequencing problem.
- Author
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Sun, Yuan, Esler, Samuel, Thiruvady, Dhananjay, Ernst, Andreas T., Li, Xiaodong, and Morgan, Kerri
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OPTIMIZATION algorithms ,MACHINE learning ,COMBINATORIAL optimization ,RESEARCH questions ,SEQUENCE analysis - Abstract
We investigate an important research question for solving the car sequencing problem, that is, which characteristics make an instance hard to solve? To do so, we carry out an instance space analysis for the car sequencing problem, by extracting a vector of problem features to characterize an instance. In order to visualize the instance space, the feature vectors are projected onto a 2-D space using dimensionality reduction techniques. The resulting 2-D visualizations provide new insights into the characteristics of the instances used for testing and how these characteristics influence the behaviours of an optimization algorithm. This analysis guides us in constructing a new set of benchmark instances with a range of instance properties. We demonstrate that these new instances are more diverse than the previous benchmarks, including some instances that are significantly more difficult to solve. We introduce two new algorithms for solving the car sequencing problem and compare them with four existing methods from the literature. Our new algorithms are shown to perform competitively for this problem but no single algorithm can outperform all others over all instances. This observation motivates us to build an algorithm selection model based on machine learning, to identify the niche in the instance space that an algorithm is expected to perform well on. Our analysis helps to understand problem hardness and select an appropriate algorithm for solving a given car sequencing problem instance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
16. Applying algorithm selection to abductive diagnostic reasoning
- Author
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Koitz-Hristov, Roxane and Wotawa, Franz
- Published
- 2018
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17. A machine learning-based selection approach for solving the single machine scheduling problem with Early/Tardy jobs
- Author
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Ahmed Adnane Abdessemed, Leila Hayet Mouss, Khaled Benaggoune, and Toufik Bentrcia
- Subjects
single machine scheduling problem ,early/tardy jobs ,algorithm selection ,machine learning ,meta-heuristics ,Economics as a science ,HB71-74 - Abstract
Today, the algorithm selection paradigm has become one of the promising approaches in the field of optimization problems. Its main goal is to solve each case of an optimization problem with the most accurate algorithm using machine learning techniques. This paper treats the issue of the algorithm selection for the Single Machine Scheduling Problem with Early/Tardy jobs by adapting three meta-heuristics from the state-of-the-art, namely genetic algorithm, particle swarm optimization, and tabu search. In the proposed framework, we combine the running time and the cost function to get a new performance criterion. A large set composed of 98000 instances of the problem is generated with 12 features characterizing each instance. We carry a statistical comparison of the implemented meta-heuristics, and we evaluate 10 classifiers. It can be deduced that the Dagging algorithm combined with the Random Forest is the most likely to be the best classifier, which achieves 88.44% of the maximum accuracy.
- Published
- 2024
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18. Taking the human out of decomposition-based optimization via artificial intelligence, Part I: Learning when to decompose.
- Author
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Mitrai, Ilias and Daoutidis, Prodromos
- Subjects
- *
ARTIFICIAL intelligence , *DEEP learning , *REPRESENTATIONS of graphs , *GRAPH neural networks , *NONLINEAR programming , *APPROXIMATION algorithms - Abstract
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. • A deep learning framework is proposed to decide when to use a decomposition. • A feature-based graph representation of optimization problems is proposed. • The proposed approach is applied to Mixed Integer Nonlinear optimization problems. • Integration of the proposed approach with optimization solvers is discussed. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
19. A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection
- Author
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Irfan Khan, Xianchao Zhang, Mobashar Rehman, and Rahman Ali
- Subjects
Meta-learning ,algorithm selection ,classification ,machine learning ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Classification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of research in machine learning community. Meta-learning has demonstrated substantial success in solving ASP, especially in the domain of classification. Considerable progress has been made in classification algorithm recommendation and researchers have proposed various methods in literature that tackles ASP in many different ways in meta-learning setup. Yet there is a lack of survey and comparative study that critically analyze, summarize and assess the performance of existing methods. To fill these gaps, in this paper we first present a literature survey of classification algorithm recommendation methods. The survey shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifier selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we critically analyzed and summarized the existing studies from the literature in three important dimensions i.e., meta-features, meta-learner and meta-target. In the second part of this paper, we present extensive comparative evaluation of all the prominent methods for classifier selection based on 17 classification algorithms and 84 benchmark datasets. The comparative study quantitatively assesses the performance of classifier selection methods and highlight the limitations and strengths of meta-features, meta-learners and meta-target in classification algorithm recommendation system. Finally, we conclude this paper by identifying current challenges and suggesting future work directions. We expect that this work will provide baseline and a solid overview of state of the art works in this domain to new researchers, and will steer future research in this direction.
- Published
- 2020
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- View/download PDF
20. Choosing Solution Strategies for Scheduling Automated Guided Vehicles in Production Using Machine Learning
- Author
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Felicia Schweitzer, Günter Bitsch, and Louis Louw
- Subjects
AGV scheduling ,optimization ,constraint programming ,machine learning ,algorithm selection ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Artificial intelligence is considered to be a significant technology for driving the future evolution of smart manufacturing environments. At the same time, automated guided vehicles (AGVs) play an essential role in manufacturing systems due to their potential to improve internal logistics by increasing production flexibility. Thereby, the productivity of the entire system relies on the quality of the schedule, which can achieve production cost savings by minimizing delays and the total makespan. However, traditional scheduling algorithms often have difficulties in adapting to changing environment conditions, and the performance of a selected algorithm depends on the individual scheduling problem. Therefore, this paper aimed to analyze the scheduling problem classes of AGVs by applying design science research to develop an algorithm selection approach. The designed artifact addressed a catalogue of characteristics that used several machine learning algorithms to find the optimal solution strategy for the intended scheduling problem. The contribution of this paper is the creation of an algorithm selection method that automatically selects a scheduling algorithm, depending on the problem class and the algorithm space. In this way, production efficiency can be increased by dynamically adapting the AGV schedules. A computational study with benchmark literature instances unveiled the successful implementation of constraint programming solvers for solving JSSP and FJSSP scheduling problems and machine learning algorithms for predicting the most promising solver. The performance of the solvers strongly depended on the given problem class and the problem instance. Consequently, the overall production performance increased by selecting the algorithms per instance. A field experiment in the learning factory at Reutlingen University enabled the validation of the approach within a running production scenario.
- Published
- 2023
- Full Text
- View/download PDF
21. Dynamic algorithm selection for pareto optimal set approximation.
- Author
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Steponavičė, Ingrida, Hyndman, Rob, Smith-Miles, Kate, and Villanova, Laura
- Subjects
APPROXIMATION algorithms ,PARETO analysis ,MACHINE learning ,PREDICTION models ,SIMULATION methods & models - Abstract
This paper presents a meta-algorithm for approximating the Pareto optimal set of costly black-box multiobjective optimization problems given a limited number of objective function evaluations. The key idea is to switch among different algorithms during the optimization search based on the predicted performance of each algorithm at the time. Algorithm performance is modeled using a machine learning technique based on the available information. The predicted best algorithm is then selected to run for a limited number of evaluations. The proposed approach is tested on several benchmark problems and the results are compared against those obtained using any one of the candidate algorithms alone. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
22. Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity.
- Author
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Barbudo, Rafael, Ramírez, Aurora, and Romero, José Raúl
- Abstract
The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper-parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and machine learning algorithms, coupled with their best hyper-parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow. Addressing this gap, this paper introduces EvoFlow , a grammar-based evolutionary approach for AWC. EvoFlow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFlow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow builds an ensemble whose workflows differ in their misclassified instances. To evaluate EvoFlow 's effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFlow 's unique components. Then, we compare EvoFlow with other AWC approaches, encompassing both evolutionary and non-evolutionary techniques. Our findings show that EvoFlow 's specialised genetic operators and updating mechanism substantially outperform current leading methods in predictive performance. Additionally, EvoFlow is capable of discovering workflow structures that other approaches in the literature have not considered. • A grammar-guided genetic programming approach for automated workflow composition. • The grammar support workflows with more flexible structure than previous methods. • An ensemble of workflows is built to guarantee diversity in terms of predictions. • Our method outperforms the existing methods in the literature. • Discovery of a wider spectrum of solutions than the baseline evolutionary method. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
23. AUTOMATIC HYPERPARAMETER OPTIMIZATION FOR CLUSTERING ALGORITHMS WITH REINFORCEMENT LEARNIN
- Author
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S. B. Muravyov, V. A. Efimova, V. V. Shalamov, A. A. Filchenkov, and I. B. Smetannikov
- Subjects
machine learning ,clustering ,algorithm selection ,hyperparameter optimization ,multi-armed bandit ,reinforcement learning ,Optics. Light ,QC350-467 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Subject of Research. The paper deals with research of clustering algorithms for hyperparameters optimization used in machine learning. Model selection problem is comprehensively studied, and the need of the tradeoff between exploration and exploitation is identified. Thus, the problem is reduced to multi-armed bandit problem. Method. The paper presented the approach for simultaneous algorithm selection and hyperparameters optimization. We used solution of the Multiarmed Bandit problem and considered Softmax- and UCB1-based algorithm variants in combination with different reward functions. Main Results. Experiments on various datasets from UCI repository were carried out. The results of experiments confirmed that proposed algorithms in general achieve significantly better results than exhaustive search method. It also helped to determine the most promising version of the algorithm we propose. Practical Relevance. The suggested algorithm can be successfully used for model selection and configuration for clustering algorithms, and can be applied in a wide range of clustering tasks in various areas, including biology, psychology, and image analysis.
- Published
- 2019
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- View/download PDF
24. Isolation forests and landmarking-based representations for clustering algorithm recommendation using meta-learning.
- Author
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Gabbay, Itay, Shapira, Bracha, and Rokach, Lior
- Subjects
- *
ALGORITHMS , *SOCIAL dominance , *COMPUTATIONAL complexity , *DOCUMENT clustering - Abstract
The data clustering problem can be described as the task of organizing data into groups, where in each group the objects share some similar attributes. Most of the problems clustering algorithms address do not have a prior solution. This paper addresses the algorithm selection challenge for data clustering, while taking the difficulty in evaluating clustering solutions into account. We present a new meta-learning method for recommending the most suitable clustering algorithm for a dataset. Based on concepts from the isolation forest algorithm, we propose a new similarity measure between datasets. Our proposed dataset characterization methods generate an embedding for a dataset using this similarity measure, which is then used to improve the quality of the problem's characterization. The method utilizes landmarking concepts to characterize the dataset and then, inspired by the DeepFM algorithm, applies meta-learning to rank the candidate algorithms that are expected to perform the best for the current dataset. This ranking could, among other things, support AutoML systems. Our approach is evaluated on a corpus of 100 publicly available benchmark datasets. We compare our method's ranking performance to that of existing meta-learning methods and show the dominance of our method in terms of predictive performance and computational complexity. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
25. Algorithm selection using edge ML and case-based reasoning
- Author
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Rahman Ali, Muhammad Sadiq Hassan Zada, Asad Masood Khatak, and Jamil Hussain
- Subjects
Algorithm selection ,Machine learning ,Meta learning ,Edge ML ,Edge computing ,Computer engineering. Computer hardware ,TK7885-7895 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Abstract In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data scarcity mitigation through incremental learning, and an open-source CBR module, reshaping research paradigms. The CBR module, trained on 100 datasets and tested with 52 datasets using 9 decision tree algorithms, achieved an accuracy of 94% for correct classifier recommendations within the top k=3 algorithms, making it highly suitable for practical classification applications.
- Published
- 2023
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- View/download PDF
26. ASAP: An Automatic Algorithm Selection Approach for Planning.
- Author
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Vallati, Mauro, Chrpa, Lukáš, and Kitchin, Diane
- Subjects
COMPUTER algorithms ,BENCHMARK problems (Computer science) ,ENCODING ,MACHINE learning ,MATHEMATICAL domains - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
27. Micro-MetaStream: Algorithm selection for time-changing data.
- Author
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Rossi, André Luis Debiaso, Soares, Carlos, Souza, Bruno Feres de, and Ponce de Leon Ferreira de Carvalho, André Carlos
- Subjects
- *
MACHINE learning , *DATA mining , *DATA distribution , *ALGORITHMS , *METADATA - Abstract
• Micro-MetaStream selects the best learning algorithm for each example of data streams. • This method relates data characteristics and the performance of regression models over time. • It combines characteristics from training data and raw features from the predictive attributes of each test example. • Predictive performance was improved when compared with two other meta-learning methods. Data stream mining needs to deal with scenarios where data distribution can change over time. As a result, different learning algorithms can be more suitable in different time periods. This paper proposes micro-MetaStream, a meta-learning based method to recommend the most suitable learning algorithm for each new example arriving in a data stream. It is an evolution of MetaStream, which recommends learning algorithms for batches of examples. By using a unitary granularity, micro-MetaStream is able to respond more efficiently to changes in data distribution than its predecessor. The meta-data combines meta-features, characteristics describing recent data, with base-level features, the original variables of the new example. In experiments on real-world regression data streams, micro-metaStream outperformed MetaStream and a baseline method at the meta-level and frequently improved the predictive performance at the base-level. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
28. 3D meta-classification: A meta-learning approach for selecting 3D point-cloud classification algorithm.
- Author
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Xu, Fan, Chen, Jun, Shi, Yizhou, Ruan, Tianchen, Wu, Qihui, and Zhang, Xiaofei
- Subjects
- *
CLASSIFICATION algorithms , *MACHINE learning , *POINT cloud , *CLASSIFICATION , *ALGORITHMS - Abstract
Algorithm selection technology, aimed at choosing the most suitable algorithm for a given machine learning task, has achieved significant success in the domain of 2D vision. However, few studies have explored its application to the 3D point cloud domain. The rapid proliferation and development of 3D point cloud classification algorithms underscore the urgent need for exploration in selecting these algorithms. In this paper, we propose a novel meta-learning-based 3D classification approach, termed 3D meta-classification , to address this gap. The approach operates at both the base-level and meta-level phases. At the base level, candidate 3D classification algorithms constantly classify various 3D datasets, recording their classification performance as empirical knowledge. As for the meta-level phase, it specifically tailors the 3D meta-knowledge generator, 3D meta-feature extractor, and 3D meta-database constructor to establish the 3D meta-database, capturing the relationship between the 3D meta-features and empirical knowledge. Leveraging the 3D meta-database, the 3D classification meta-learner trains the meta-model and predicts suitable algorithms for new incoming 3D datasets. Extensive experiments are conducted to select the best-performing classification algorithm for specific 3D datasets from ModelNet40. The results demonstrate the effectiveness of the proposed 3D meta-classification model; the accuracies of one-from-two and one-from-three algorithm selection tasks reach over 98% and 80%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
29. Instance Space Analysis for Algorithm Testing: Methodology and Software Tools.
- Author
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SMITH-MILES, KATE and MUÑOZ, MARIO ANDRÉS
- Subjects
SOFTWARE development tools ,COMPUTER software testing ,ALGORITHMS ,SOFTWARE as a service - Abstract
Instance Space Analysis (ISA) is a recently developed methodology to (a) support objective testing of algorithms and (b) assess the diversity of test instances. Representing test instances as feature vectors, the ISA methodology extends Rice’s 1976 Algorithm Selection Problem framework to enable visualization of the entire space of possible test instances, and gain insights into how algorithm performance is affected by instance properties. Rather than reporting algorithm performance on average across a chosen set of test problems, as is standard practice, the ISA methodology offers a more nuanced understanding of the unique strengths and weaknesses of algorithms across different regions of the instance space that may otherwise be hidden on average. It also facilitates objective assessment of any bias in the chosen test instances and provides guidance about the adequacy of benchmark test suites. This article is a comprehensive tutorial on the ISA methodology that has been evolving over several years, and includes details of all algorithms and software tools that are enabling its worldwide adoption in many disciplines. A case study comparing algorithms for university timetabling is presented to illustrate the methodology and tools. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
30. Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges.
- Author
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Muñoz, Mario A., Sun, Yuan, Kirley, Michael, and Halgamuge, Saman K.
- Subjects
- *
ALGORITHMS , *MATHEMATICAL optimization , *PROBLEM solving , *NUMERICAL calculations , *INFORMATION theory - Abstract
Selecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed. In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice’s (1976) selection framework. We describe each of the four component spaces – problem, algorithm, performance and characteristic – in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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- View/download PDF
31. Machine Learning Based Adaptive Contour Detection Using Algorithm Selection and Image Splitting.
- Author
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LUKAC, Martin, TANIZAWA, Riichi, and KAMEYAMA, Michitaka
- Subjects
ARTIFICIAL intelligence ,ALGORITHMS ,COMPUTER graphics ,IMAGE processing - Abstract
In real world images, many algorithms for adaptive contours detection exist and various improvements to the contours detection have been proposed. The reason for such diversity is that real world images contains heterogeneous mixtures of features and each of the available algorithms exploits some of these features. Thus, depending on the image, different algorithms shows different quality of result. In this paper we propose a method that improves the result adaptive contours detection by using an algorithm selection approach. Previous methods using the algorithm selection approach have been focusing only on images with a particular class of features (artificial, cellular) because of the complexity of real world images. In order to successfully solve this problem we first determine a set of distinctive features of each algorithm using machine learning. Then using these distinctive features we teach an algorithm selector to select best algorithm when a set of features is provided. Finally, we propose a method to split the input image into sub regions that are selected in such a manner that improves the quality of the image processing result. The proposed algorithm is verified on the set of benchmarks and its performance is comparable and better in many cases than the currently best contour detection algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
32. Performance Modeling and Algorithm Characterization for Robust Image Segmentation.
- Author
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Shah, Shishir
- Subjects
DISTRIBUTION (Probability theory) ,DIGITAL image processing ,COMPUTER vision ,PATTERN recognition systems ,ARTIFICIAL intelligence ,BAYES' theorem ,GAUSSIAN processes - Abstract
This paper presents a probabilistic framework based on Bayesian theory for the performance prediction and selection of an optimal segmentation algorithm. The framework models the optimal algorithm selection process as one that accounts for the information content of an input image as well as the behavioral properties of a particular candidate segmentation algorithm. The input image information content is measured in terms of image features while the candidate segmentation algorithm’s behavioral characteristics are captured through the use of segmentation quality features. Gaussian probability distribution models are used to learn the required relationships between the extracted image and algorithm features and the framework tested on the Berkeley Segmentation Dataset using four candidate segmentation algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2008
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- View/download PDF
33. APPLYING MACHINE LEARNING TO LOW-KNOWLEDGE CONTROL OF OPTIMIZATION ALGORITHMS.
- Author
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Carchrae, Tom and Beck, J. Christopher
- Subjects
MACHINE learning ,COMPUTER algorithms ,MATHEMATICAL optimization ,PRODUCTION scheduling ,ARTIFICIAL intelligence ,COMPUTATIONAL intelligence - Abstract
This paper addresses the question of allocating computational resources among a set of algorithms to achieve the best performance on scheduling problems. Our primary motivation in addressing this problem is to reduce the expertise needed to apply optimization technology. Therefore, we investigate algorithm control techniques that make decisions based only on observations of the improvement in solution quality achieved by each algorithm. We call our approach “low knowledge” since it does not rely on complex prediction models, either of the problem domain or of algorithm behavior. We show that a low-knowledge approach results in a system that achieves significantly better performance than all of the pure algorithms without requiring additional human expertise. Furthermore the low-knowledge approach achieves performance equivalent to a perfect high-knowledge classification approach. [ABSTRACT FROM AUTHOR]
- Published
- 2005
- Full Text
- View/download PDF
34. Autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithms
- Author
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Moncef Garouani, Adeel Ahmad, Mourad Bouneffa, and Mohamed Hamlich
- Subjects
Algorithm selection ,AutoML ,Meta-learning ,Meta-features ,Data representation ,kNN ,Computer engineering. Computer hardware ,TK7885-7895 ,Information technology ,T58.5-58.64 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Abstract The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the configuration of its hyperparameters is among the most complicated tasks while applying ML to new problems. It necessitates well awareness and knowledge of ML algorithms. The algorithm selection problem (ASP) is defined as the process of identifying the algorithm (s) that can deliver top performance for a particular problem, task, and evaluation measure. In this context, meta-learning is one of the approaches to achieve this objective by using prior learning experiences to assist the learning process on unseen problems and tasks. As a data-driven approach, appropriate data characterization is of vital importance for the meta-learning. Nonetheless, the recent literature witness a variety of data characterization techniques including simple, statistical and information theory based measures. However, their quality still needs to be improved. In this paper, a new Autoencoder-kNN (AeKNN) based meta-model with built-in latent features extraction is proposed. The approach is aimed to extract new characterizations of the data, with lower dimensionality but more significant and meaningful features. AeKNN internally uses a deep autoencoder as a latent features extractor from a set of existing meta-features induced from the dataset. From this new features vectors the computed distances are more significant, thus providing a way to accurately recommending top-performing pipelines for previously unseen datasets. In an application on a large-scale hyperparameters optimization task for 400 real world datasets with varying schemas as a meta-learning task, we show that AeKNN offers considerable improvements of the classical kNN as well as traditional meta-models in terms of performance.
- Published
- 2023
- Full Text
- View/download PDF
35. CNN-HT: A Two-Stage Algorithm Selection Framework
- Author
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Siyi Xu, Wenwen Liu, Chengpei Wu, and Junli Li
- Subjects
algorithm selection ,convolutional neural network ,exploratory landscape analysis ,classification ,feature selection ,hypothesis testing ,Science ,Astrophysics ,QB460-466 ,Physics ,QC1-999 - Abstract
The No Free Lunch Theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. This paper introduces an innovative algorithm selection approach called the CNN-HT, which is a two-stage algorithm selection framework. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the statistical analysis of the performance metric of algorithms that address various problem categories. The two-stage approach can adapt to different algorithm combinations without the need to retrain the entire model, and modifications can be made in the second stage only, which is an improvement of one-stage approaches. To provide a more general structure for the classification model, we adopt Exploratory Landscape Analysis (ELA) features of the problem as input and utilize feature selection techniques to reduce the redundant ones. In problem classification, the average accuracy of classifying problems using CNN is 96%, which demonstrates the advantages of CNN compared to Random Forest and Support Vector Machines. After feature selection, the accuracy increases to 98.8%, further improving the classification performance while reducing the computational cost. This demonstrates the effectiveness of the first stage of the CNN-HT method, which provides a basis for algorithm selection. In the experiments, CNN-HT shows the advantages of the second stage algorithm as well as good performance with better average rankings in different algorithm combinations compared to the individual algorithms and another algorithm combination approach.
- Published
- 2024
- Full Text
- View/download PDF
36. Reinforcement-based Method for Simultaneous Clustering Algorithm Selection and its Hyperparameters Optimization.
- Author
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Shalamov, Viacheslav, Efimova, Valeria, Muravyov, Sergey, and Filchenkov, Andrey
- Subjects
DOCUMENT clustering ,REINFORCEMENT learning ,ALGORITHMS ,PARALLEL algorithms ,MATHEMATICAL optimization - Abstract
Abstract A wide range of clustering algorithms exists, most of them expose many hyperparameters, on which clustering partition quality depends. Simultaneous algorithm (model) selection and its hyperparameters optimization is considered to be a sophisticated task, which is known according to some sources as combined algorithm selection and hyperparameter optimization. In this paper, we focus on problem of selecting a clustering algorithm and its hyperparameter vector simultaneously given a dataset in order to achieve the best partition quality. We propose a method for selecting a proper clustering algorithm and its hyperparameter vector using reinforcement learning. Instead of tuning hyperparameters for all available clustering algorithms and selecting one showing the best performance, we make them to compete for time that they can use for optimizing their own hyperparameters. In our algorithm, we use a framework for multi-armed bandit problem, which is a special case of reinforcement learning. Each clustering algorithm is considered as an arm in the multi-armed bandit setting, while assigning a time budget to optimize hyperparameters of a clustering algorithm is considered as playing the corresponding arm. We conducted series of experiments for comparing out reinforcement learning approach to the classical exhaustive search approach. We conducted experiments on 20 datasets from UCI Repository such as Iris, haberman, krvskp, glass and other. We use 19 cluster validity indices to validate the clusters, built by selected and configured algorithm. As a hyperparameter optimization algorithm, we used SMAC. Our approach managed to improve model selection and hyperparameter optimization process, by sustaining the exploration-exploitation trade-off and spending available time budget more wisely. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
37. User preferences based software defect detection algorithms selection using MCDM
- Author
-
Peng, Yi, Wang, Guoxun, and Wang, Honggang
- Subjects
- *
DATA mining , *TEXT mining , *COMPUTER algorithms , *MULTIPLE criteria decision making , *COMPUTER software , *COMPUTER users , *FREEWARE (Computer software) , *PERFORMANCE evaluation - Abstract
Abstract: A variety of classification algorithms for software defect detection have been developed over the years. How to select an appropriate classifier for a given task is an important issue in Data mining and knowledge discovery (DMKD). Many studies have compared different types of classification algorithms and the performances of these algorithms may vary using different performance measures and under different circumstances. Since the algorithm selection task needs to examine several criteria, such as accuracy, computational time, and misclassification rate, it can be modeled as a multiple criteria decision making (MCDM) problem. The goal of this paper is to use a set of MCDM methods to rank classification algorithms, with empirical results based on the software defect detection datasets. Since the preferences of the decision maker (DM) play an important role in algorithm evaluation and selection, this paper involved the DM during the ranking procedure by assigning user weights to the performance measures. Four MCDM methods are examined using 38 classification algorithms and 13 evaluation criteria over 10 public-domain software defect datasets. The results indicate that the boosting of CART and the boosting of C4.5 decision tree are ranked as the most appropriate algorithms for software defect datasets. Though the MCDM methods provide some conflicting results for the selected software defect datasets, they agree on most top-ranked classification algorithms. [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
38. Automated streamliner portfolios for constraint satisfaction problems.
- Author
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Spracklen, Patrick, Dang, Nguyen, Akgün, Özgür, and Miguel, Ian
- Subjects
- *
CONSTRAINT satisfaction , *CONSTRAINT programming , *PROBLEM solving , *SOUND design - Abstract
Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial problems. Solving a problem proceeds in two distinct phases: modelling and solving. Effective modelling has a huge impact on the performance of the solving process. Even with the advance of modern automated modelling tools, search spaces involved can be so vast that problems can still be difficult to solve. To further constrain the model, a more aggressive step that can be taken is the addition of streamliner constraints, which are not guaranteed to be sound but are designed to focus effort on a highly restricted but promising portion of the search space. Previously, producing effective streamlined models was a manual, difficult and time-consuming task. This paper presents a completely automated process to the generation, search and selection of streamliner portfolios to produce a substantial reduction in search effort across a diverse range of problems. The results demonstrate a marked improvement in performance for both Chuffed, a CP solver with clause learning, and lingeling, a modern SAT solver. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Automated Algorithm Selection for Radar Network Configuration
- Author
-
Renau, Quentin, Dreo, Johann, Peres, Alain, Semet, Yann, Doerr, Carola, Doerr, Benjamin, Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP), THALES [France], Recherche Opérationnelle (RO), LIP6, and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,exploratory landscape analysis ,evolutionary computation ,Computer Science - Neural and Evolutionary Computing ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,Neural and Evolutionary Computing (cs.NE) ,radar network configuration ,[INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] ,algorithm selection ,Machine Learning (cs.LG) - Abstract
The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed. Classic optimization algorithms can therefore not be applied to this problem, and we rely on "trial-and-error" black-box approaches. In this paper, we study the performances of 13 black-box optimization algorithms on 153 radar network configuration problem instances. The algorithms perform considerably better than human experts. Their ranking, however, depends on the budget of configurations that can be evaluated and on the elevation profile of the location. We therefore also investigate automated algorithm selection approaches. Our results demonstrate that a pipeline that extracts instance features from the elevation of the terrain performs on par with the classical, far more expensive approach that extracts features from the objective function., Author-generated version of a paper in the proceedings of The Genetic and Evolutionary Computation Conference 2022 (GECCO 2022)
- Published
- 2022
40. Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization.
- Author
-
Muñoz, Mario Andrés and Kirley, Michael
- Subjects
ALGORITHMS ,STATISTICAL sampling - Abstract
In this paper, we investigate how systemic errors due to random sampling impact on automated algorithm selection for bound-constrained, single-objective, continuous black-box optimization. We construct a machine learning-based algorithm selector, which uses exploratory landscape analysis features as inputs. We test the accuracy of the recommendations experimentally using resampling techniques and the hold-one-instance-out and hold-one-problem-out validation methods. The results demonstrate that the selector remains accurate even with sampling noise, although not without trade-offs. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
41. Construction of Verifier Combinations Based on Off-the-Shelf Verifiers
- Author
-
Beyer, Dirk, Kanav, Sudeep, Richter, Cedric, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Johnsen, Einar Broch, editor, and Wimmer, Manuel, editor
- Published
- 2022
- Full Text
- View/download PDF
42. Alors: An algorithm recommender system.
- Author
-
Mısır, Mustafa and Sebag, Michèle
- Subjects
- *
RECOMMENDER systems , *COMPUTER algorithms , *INFORMATION filtering , *CONSTRAINT programming , *CONSTRAINT satisfaction - Abstract
Algorithm selection (AS), selecting the algorithm best suited for a particular problem instance, is acknowledged to be a key issue to make the best out of algorithm portfolios. This paper presents a collaborative filtering approach to AS. Collaborative filtering, popularized by the Netflix challenge, aims to recommend the items that a user will most probably like, based on the previous items she liked, and the items that have been liked by other users. As first noted by Stern et al. [47] , algorithm selection can be formalized as a collaborative filtering problem, by considering that a problem instance “likes better“ the algorithms that achieve better performance on this particular instance. Two merits of collaborative filtering (CF) compared to the mainstream algorithm selection (AS) approaches are the following. Firstly, mainstream AS requires extensive and computationally expensive experiments to learn a performance model, with all algorithms launched on all problem instances, whereas CF can exploit a sparse matrix, with a few algorithms launched on each problem instance. Secondly, AS learns a performance model as a function of the initial instance representation, whereas CF builds latent factors to describe algorithms and instances, and uses the associated latent metrics to recommend algorithms for a specific problem instance. A main contribution of the proposed algorithm recommender Alors system is to handle the cold start problem – emitting recommendations for a new problem instance – through the non-linear modeling of the latent factors based on the initial instance representation, extending the linear approach proposed by Stern et al. [47] . The merits and generality of Alors are empirically demonstrated on the ASLib [6] and OpenML [53] benchmarks. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
43. Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches.
- Author
-
Alissa, Mohamad, Sim, Kevin, and Hart, Emma
- Subjects
ALGORITHMS ,RECURRENT neural networks ,MACHINE learning ,DEEP learning ,HEURISTIC - Abstract
We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types of recurrent neural networks to predict a packing heuristic in online bin-packing, selecting from four well-known heuristics. As input, the RNN methods only use the sequence of item-sizes. This contrasts to typical approaches to algorithm-selection which require a model to be trained using domain-specific instance features that need to be first derived from the input data. The RNN approaches are shown to be capable of achieving within 5% of the oracle performance on between 80.88 and 97.63% of the instances, depending on the dataset. They are also shown to outperform classical machine learning models trained using derived features. Finally, we hypothesise that the proposed methods perform well when the instances exhibit some implicit structure that results in discriminatory performance with respect to a set of heuristics. We test this hypothesis by generating fourteen new datasets with increasing levels of structure, and show that there is a critical threshold of structure required before algorithm-selection delivers benefit. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
44. H3AD: A hybrid hyper-heuristic for algorithm design.
- Author
-
Miranda, Péricles B.c., Prudêncio, Ricardo B.c., and Pappa, Gisele L.
- Subjects
- *
ALGORITHM software , *HEURISTIC , *MACHINE learning , *PARTICLE swarm optimization , *SOCIAL interaction - Abstract
Designing an algorithm to solve a given problem is a challenging task due to the variety of possible design choices and the lack of clear guidelines on how to choose and/or combine them. Optimization and machine learning techniques have been used to make the algorithm design process more independent on human intervention. Hyper-heuristic approaches, in particular, have been proposed to search the space of algorithms/heuristics and/or their components, and iteratively combine and adapt them for specific problems. Although flexible to produce customized algorithms, hyper-heuristics can be extremely costly procedures. This paper proposes a novel hybrid hyper-heuristic (H3AD), which combines an automated algorithm selection approach with a generative hyper-heuristic. This combination intends to reduce the cost of providing an algorithm for a new input problem by reusing algorithms previously built by hyper-heuristics to solve similar problems. H3AD was evaluated in a case study to optimize the design of Particle Swarm Optimization algorithms in unconstrained continuous optimization problems. The results showed that H3AD provided appropriate recommendations of algorithms, reusing the algorithms generated by the hyper-heuristic to new input problems. Besides, H3AD drastically reduced the time of providing a customized algorithm when compared to generative hyper-heuristics, without a significant loss of optimization performance. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
45. A Literature Survey and Empirical Study of Meta-Learning for Classifier Selection
- Author
-
Mobashar Rehman, Irfan Khan, Rahman Ali, and Xianchao Zhang
- Subjects
0209 industrial biotechnology ,General Computer Science ,Computer science ,02 engineering and technology ,Recommender system ,Machine learning ,computer.software_genre ,Algorithm Selection ,Comparative evaluation ,020901 industrial engineering & automation ,Empirical research ,Meta-learning ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,business.industry ,General Engineering ,Statistical classification ,machine learning ,classification ,020201 artificial intelligence & image processing ,Artificial intelligence ,Selection method ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,Literature survey ,computer ,Classifier (UML) ,algorithm selection ,lcsh:TK1-9971 - Abstract
Classification is the key and most widely studied paradigm in machine learning community. The selection of appropriate classification algorithm for a particular problem is a challenging task, formally known as algorithm selection problem (ASP) in literature. It is increasingly becoming focus of research in machine learning community. Meta-learning has demonstrated substantial success in solving ASP, especially in the domain of classification. Considerable progress has been made in classification algorithm recommendation and researchers have proposed various methods in literature that tackles ASP in many different ways in meta-learning setup. Yet there is a lack of survey and comparative study that critically analyze, summarize and assess the performance of existing methods. To fill these gaps, in this paper we first present a literature survey of classification algorithm recommendation methods. The survey shed light on the motivational reasons for pursuing classifier selection through meta-learning and comprehensively discusses the different phases of classifier selection based on a generic framework that is formed as an outcome of reviewing prior works. Subsequently, we critically analyzed and summarized the existing studies from the literature in three important dimensions i.e., meta-features, meta-learner and meta-target. In the second part of this paper, we present extensive comparative evaluation of all the prominent methods for classifier selection based on 17 classification algorithms and 84 benchmark datasets. The comparative study quantitatively assesses the performance of classifier selection methods and highlight the limitations and strengths of meta-features, meta-learners and meta-target in classification algorithm recommendation system. Finally, we conclude this paper by identifying current challenges and suggesting future work directions. We expect that this work will provide baseline and a solid overview of state of the art works in this domain to new researchers, and will steer future research in this direction.
- Published
- 2020
46. MachSMT: A Machine Learning-based Algorithm Selector for SMT Solvers
- Author
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Scott, Joseph, Niemetz, Aina, Preiner, Mathias, Nejati, Saeed, Ganesh, Vijay, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Groote, Jan Friso, editor, and Larsen, Kim Guldstrand, editor
- Published
- 2021
- Full Text
- View/download PDF
47. Cascaded Algorithm Selection With Extreme-Region UCB Bandit.
- Author
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Hu, Yi-Qi, Liu, Xu-Hui, Li, Shu-Qiao, and Yu, Yang
- Subjects
ROBBERS ,SOFTWARE engineering ,PSYCHOLOGICAL feedback - Abstract
AutoML aims at best configuring learning systems automatically. It contains core subtasks of algorithm selection and hyper-parameter tuning. Previous approaches considered searching in the joint hyper-parameter space of all algorithms, which forms a huge but redundant space and causes an inefficient search. We tackle this issue in a cascaded algorithm selection way, which contains an upper-level process of algorithm selection and a lower-level process of hyper-parameter tuning for algorithms. While the lower-level process employs an anytime tuning approach, the upper-level process is naturally formulated as a multi-armed bandit, deciding which algorithm should be allocated one more piece of time for the lower-level tuning. To achieve the goal of finding the best configuration, we propose the Extreme-Region Upper Confidence Bound (ER-UCB) strategy. Unlike UCB bandits that maximize the mean of feedback distribution, ER-UCB maximizes the extreme-region of feedback distribution. We first consider stationary distributions and propose the ER-UCB-S algorithm that has $O(K\ln n)$ O (K ln n) regret upper bound with $K$ K arms and $n$ n trials. We then extend to non-stationary settings and propose the ER-UCB-N algorithm that has $O(Kn^\nu)$ O (K n ν) regret upper bound, where $\frac{2}{3}<\nu <1$ 2 3 < ν < 1 . Finally, empirical studies on synthetic and AutoML tasks verify the effectiveness of ER-UCB-S/N by their outperformance in corresponding settings. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
48. Algorithm Selection Framework for Legalization Using Deep Convolutional Neural Networks and Transfer Learning.
- Author
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Netto, Renan, Fabre, Sheiny, Fontana, Tiago Augusto, Livramento, Vinicius, Pilla, Laercio L., Behjat, Laleh, and Guntzel, Jose Luis
- Subjects
CONVOLUTIONAL neural networks ,LEGALIZATION ,DEEP learning ,TRANSFER of training ,ALGORITHMS ,MACHINE learning - Abstract
Machine learning (ML) models have been used to improve the quality of different physical design steps, such as timing analysis, clock tree synthesis, and routing. However, so far very few works have addressed the problem of algorithm selection during physical design, which can drastically reduce the computational effort of some steps. This work proposes a legalization algorithm selection framework using deep convolutional neural networks (CNNs). To extract features, we used snapshots of circuit placements and used transfer learning to train the models using pretrained weights of the Squeezenet architecture. By doing so, we can greatly reduce the training time and required data even though the pretrained weights come from a different problem. We performed extensive experimental analysis of ML models, providing details on how we chose the parameters of our model, such as CNN architecture, learning rate, and number of epochs. We evaluated the proposed framework by training a model to select between different legalization algorithms according to cell displacement and wirelength variation. The trained models achieved an average $F$ -score of 0.98 when predicting cell displacement and 0.83 when predicting wirelength variation. When integrated into the physical design flow, the cell displacement model achieved the best results on 15 out of 16 designs, while the wirelength variation model achieved that for 10 out of 16 designs, being better than any individual legalization algorithm. Finally, using the proposed ML model for algorithm selection resulted in a speedup of up to $10\times $ compared to running all the algorithms separately. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
49. Exploring the role of graph spectra in graph coloring algorithm performance.
- Author
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Smith-Miles, Kate and Baatar, Davaatseren
- Subjects
- *
GRAPH theory , *GRAPH coloring , *TASK performance , *HEURISTIC algorithms , *MATHEMATICAL bounds , *MATHEMATICAL analysis - Abstract
This paper considers the challenge of recognizing how the properties of a graph determine the performance of graph coloring algorithms. In particular, we examine how spectral properties of the graph make the graph coloring task easy or hard. We address the question of when an exact algorithm is likely to provide a solution in a reasonable computation time, when we are better off using a quick heuristic, and how the properties of the graph influence algorithm performance. A new methodology is introduced to visualize a collection of graphs in an instance space defined by their properties, with an optimal feature selection approach included to ensure that the instance space preserves the topology of an algorithm performance metric. In this instance space we can reveal how different algorithms perform across a variety of instance classes with various properties. We demonstrate the methodology using a sophisticated branch and bound exact algorithm, and the faster heuristic approaches of integer rounding of a linear programming relaxation of the graph coloring formulation, as well as a greedy degree-saturation heuristic. Our results demonstrate that spectral properties of the graph instances can provide useful descriptions of when each of these algorithms will provide the best solution for a given computational effort. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
50. Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue
- Author
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Pavel Brazdil and Christophe Giraud-Carrier
- Subjects
Computer science ,media_common.quotation_subject ,Section (typography) ,02 engineering and technology ,Data science ,Metalearning ,Field (computer science) ,Algorithm Selection ,State (polity) ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Software ,media_common - Abstract
This article serves as an introduction to the Special Issue on Metalearning and Algorithm Selection. The introduction is divided into two parts. In the the first section, we give an overview of how the field of metalearning has evolved in the last 1–2 decades and mention how some of the papers in this special issue fit in. In the second section, we discuss the contents of this special issue. We divide the papers into thematic subgroups, provide information about each subgroup, as well as about the individual papers. Our main aim is to highlight how the papers selected for this special issue contribute to the field of metalearning.
- Published
- 2017
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