检索结果-内蒙古大学图书馆

Instance Space Analysis of Search-Based Software Testing

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第4期49卷 2642-2660页

作者： Neelofar, Neelofar Smith-Miles, Kate Munoz, Mario Andres Aleti, Aldeida Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

Search-based software testing (SBST) is now a mature area, with numerous techniques developed to tackle the challenging task of software testing. SBST techniques have shown promising results and have been successfully applied in the industry to automatically generate test cases for large and complex software systems. Their effectiveness, however, has been shown to be problem dependent. In this paper, we revisit the problem of objective performance evaluation of SBST techniques in light of recent methodological advances - in the form of Instance Space Analysis (ISA) - enabling the strengths and weaknesses of SBST techniques to be visualised and assessed across the broadest possible space of problem instances (software classes) from common benchmark datasets. We identify features of SBST problems that explain why a particular instance is hard for an SBST technique, reveal areas of hard and easy problems in the instance space of existing benchmark datasets, and identify the strengths and weaknesses of state-of-the-art SBST techniques. In addition, we examine the diversity and quality of common benchmark datasets used in experimental evaluations.

关键词： Automated software testing algorithm selection instance space analysis

来源：评论

学校读者我要写书评

暂无评论

Instance Space Analysis for algorithm Testing: Methodology and Software Tools

引用

ACM COMPUTING SURVEYS 2023年第12期55卷 1-31页

作者： Smith-Miles, Kate Munoz, Mario Andres Univ Melbourne Sch Math & Stat ARC Training Ctr Optimisat Technol Integrated Met Parkville Vic 3010 Australia

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.

关键词： algorithm footprints algorithm selection benchmarking MATLAB metalearning meta-heuristics software as a service test instance diversity timetabling

来源：评论

学校读者我要写书评

暂无评论

AHP Based Classification algorithm selection for Clinical Decision Support System Development

AHP Based Classification Algorithm Selection for Clinical De...

引用

Conference on Conquering Complexity - Challenges and Opportunities

作者： Khanmohammadi, Sina Rezaeiahari, Mandana SUNY Binghamton Watson Sch Engn Dept Syst Sci & Ind Engn Binghamton NY 13902 USA

Supervised classification algorithms have become very popular because of their potential application in developing intelligent data analytic software. These algorithms are known to be sensitive to the characteristic and structure of input datasets, therefore, researchers use diftbrent algorithm selection methods to select the most suitable classification algorithm for specific dataset. These methods do not consider the uncertainty about input dataset, and relative importance of different performance measurements (such as speed, accuracy, and memory usage) in the target application domain. Therefore, these methods are not appropriate for software development. This is especially true in medical field where various high dimensional noisy data might be used with the software. Hence, software developers need to select one supervised classification algorithm that has the highest- potential to provide good performance in wide variety of datasets. In this regard, an Analytic Hierarchy Process (AHP) based metalearning algorithm is proposed to identify the most suitable supervised classification algorithm for developing clinical decision support system (CDSS).The results from ten publicly available medical datasets indicate that Support Vector Machine (SVM) has the highest potential to perform well on variety of medical datasets. (C) 2014 Published by Elsevier B.V.

关键词： Clinical Decision Support System (CDSS) Machine Leaning Medical informatics algorithm selection AHP Meta-Learning

来源：评论

学校读者我要写书评

暂无评论

A Lévy Flight-Inspired Random Walk algorithm for Continuous Fitness Landscape Analysis

引用

INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2023年第1期17卷 1页

作者： Wang, Yi Li, Kangshun Guangdong Univ Sci & Technol Dongguan Peoples R China South China Agr Univ Guangzhou Peoples R China

Heuristic algorithms are effective methods for solving complex optimization problems. The optimal algorithm selection for a specific optimization problem is a challenging task. Fitness landscape analysis (FLA) is used to understand the optimization problem's characteristics and help select the optimal algorithm. A random walk algorithm is an essential technique for FLA in continuous search space. However, most currently proposed random walk algorithms suffer from unbalanced sampling points. This article proposes a Levy flight-based random walk (LRW) algorithm to address this problem. The Levy flight is used to generate the proposed random walk algorithm's variable step size and direction. Some tests show that the proposed LRW algorithm performs better in the uniformity of sampling points. Besides, the authors analyze the fitness landscape of the CEC2017 benchmark functions using the proposed LRW algorithm. The experimental results indicate that the proposed LRW algorithm can better obtain the structural features of the landscape and has better stability than several other RW algorithms.

关键词： algorithm selection Fitness landscape analysis Levy flight Random walk algorithm

来源：评论

学校读者我要写书评

暂无评论

Comprehensive algorithm Portfolio Evaluation using Item Response Theory

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1-52页

作者： Kandanaarachchi, Sevvandi Smith-Miles, Kate CSIROs Data61 Res Way Clayton Vic 3168 Australia Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

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.

关键词： Item Response Theory algorithm evaluation algorithm portfolios classification ma chine learning algorithm selection instance space analysis explainable algorithm evaluation.

来源：评论

学校读者我要写书评

暂无评论

Landscape Analysis Based vs. Domain-Specific Optimization for Engineering Design Applications: A Clear Case 2

Landscape Analysis Based vs. Domain-Specific Optimization fo...

引用

2nd IEEE Conference on Artificial Intelligence (CAI)

作者： de Winter, Roy Long, Fu Xing Thomaser, Andre Back, Thomas H. W. van Stein, Niki Kononova, Anna, V Leiden Univ LIACS Leiden Netherlands BMW Grp Munich Germany

ISBN: (纸本)9798350354102;9798350354096

Traditional methods in black-box optimization often prescribe general-purpose algorithms for broad problem categories, which overlooks the significant variability in optimization landscapes that can exist even within a single domain. We address this gap by demonstrating the efficacy of landscape analysis techniques in guiding the choice and tuning of optimization algorithms for distinct landscape features. This study focuses on two real-world problem domains characterized by blackbox simulation-based optimization: automotive control system calibration (anti-lock braking system) and automotive crashworthiness optimization. Through comparative analysis of computer experiments in these domains, landscape analysis techniques are employed for both visualization and explanation purposes. The results reveal substantial variations in landscape features across different instances within the same problem domain. Consequently, we advocate for a paradigm shift towards learning and applying optimizers tailored to specific landscape feature spaces rather than applying a one-size-fits-all approach to an entire problem domain. This research not only enhances the understanding of landscape variability in optimization problems but also paves the way for more efficient and effective optimization processes in complex, real-world scenarios.

关键词： Landscape Analysis algorithm selection Black Box Optimization Engineering Optimization

来源：评论

学校读者我要写书评

暂无评论

Selecting an appropriate supervised machine learning algorithm for predictive maintenance

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第7-8期119卷 4277-4301页

作者： Ouadah, Abdelfettah Zemmouchi-Ghomari, Leila Salhi, Nedjma ENST Ecole Natl Super Technol Ind Engn & Maintenance Dept Algiers Algeria

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.

关键词： Predictive maintenance Supervised machine learning Regression Classification Vibratory analysis Reliability algorithm selection algorithm performance Prediction Real-world data Simulation data

来源：评论

学校读者我要写书评

暂无评论

A generic method to compose an algorithm portfolio with a problem set of unknown distribution

引用

MEMETIC COMPUTING 2022年第3期14卷 287-304页

作者： Liu, Wenwen Yuen, Shiu Yin Sung, Chi Wan City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

As an individual algorithm rarely outperforms all kinds of optimization problems, algorithm portfolios are proposed to combine algorithms and take advantage of their strengths which fits well the prevalent theme of memetic computing. When there are many algorithms to choose from, the possibilities of algorithm combinations are numerous. Therefore, composing an algorithm portfolio which performs well for a given problem class is essential. In this paper, based on a problem set drawn from any unknown problem class according to an unknown probability distribution, we propose a general method to automatically accomplish portfolio construction. The problem set is used as training data for our method to learn an algorithm portfolio suitable for solving the underlying problem class. To construct the portfolio, algorithms are chosen and added one by one. We first find the best-performing algorithm based on its average rank of solving the training problem set. Its most complementary algorithm is then selected by applying the Pearson correlation coefficient of fitness values at the first hitting time. The method iterates to compose the portfolio with more and more algorithms until there is no more improvement. The experimental results indicate the effectiveness of this approach to select well-cooperated algorithms, and the composed portfolio is shown to have the best rank compared to individual algorithms, elite portfolios and comparison algorithms.

关键词： algorithm portfolio composition algorithm selection Black box optimization problem

来源：评论

学校读者我要写书评

暂无评论

A survey of fitness landscape analysis for optimization

引用

NEUROCOMPUTING 2022年 503卷 129-139页

作者： Zou, Feng Chen, Debao Liu, Hui Cao, Siyu Ji, Xuying Zhang, Yan Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China

Over past few decades, as a powerful analytical tool to characterize the fitness landscape of a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all kinds of optimization areas. Since its introduction by Sewell Wright in 1932, FLA has attracted more and more attention of researchers for its research significance and application value such as an intuitive understanding features of complex optimization problems, explaining evolutionary algorithm behavior, assessing performances of algorithms, and guiding selections and/or configures of algorithms. This paper attempts to give an overview of fitness landscape analysis and its typical application for optimization so far. We hope that this survey can help to understand features of complex optimization problems in depth and thus to improve the certain algorithm performance of for a given optimization problem.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Survey Fitness landscape Landscape analysis Heuristic algorithms algorithm selection

来源：评论

学校读者我要写书评

暂无评论

An Evaluation of Domain-Agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation 18th

An Evaluation of Domain-Agnostic Representations to Enable...

引用

18th International Conference on Learning and Intelligent Optimization, LION 2024

作者： Stone, Christopher Renau, Quentin Miguel, Ian Hart, Emma Edinburgh Napier University Edinburgh United Kingdom University of St Andrews St Andrews United Kingdom

ISBN: (纸本)9783031756221

We address the question of multi-task algorithm selection in combinatorial optimisation domains. This is motivated by a desire to simplify the algorithm-selection pipeline by developing a more general classifier that does not require specialised information per domain, and the potential for transfer learning. A minimum requirement to achieve this is to find a common representation for describing instances from multiple domains. We assess the strengths and weaknesses of three candidate representations (text, images and graphs) which can all be used to describe three different application domains. Two setups are considered: single-task selection where one classifier is trained per domain, each using the same representation, and multi-task selection where a single classifier is trained with data from all three domains to output the best solver per instance. We find that the domain-agnostic representations perform comparably with domain-specific feature-based classifiers with the benefit of providing a generic representation that does not require feature identification or computation, and could be extended to additional domains in future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： algorithm selection Combinatorial Optimisation Multi-Task Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：