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InterEvo-TR: Interactive evolutionary Test Generation With Readability Assessment

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第4期49卷 2580-2596页

作者： Delgado-Perez, Pedro Ramirez, Aurora Valle-Gomez, Kevin J. Medina-Bulo, Inmaculada Romero, Jose Raul Univ Cadiz Cadiz 11003 Spain Univ Cordoba Cordoba 14071 Spain

Automated test case generation has proven to be useful to reduce the usually high expenses of software testing. However, several studies have also noted the skepticism of testers regarding the comprehension of generated test suites when compared to manually designed ones. This fact suggests that involving testers in the test generation process could be helpful to increase their acceptance of automatically-produced test suites. In this paper, we propose incorporating interactive readability assessments made by a tester into EvoSuite, a widely-known evolutionary test generation tool. Our approach, InterEvo-TR, interacts with the tester at different moments during the search and shows different test cases covering the same coverage target for their subjective evaluation. The design of such an interactive approach involves a schedule of interaction, a method to diversify the selected targets, a plan to save and handle the readability values, and some mechanisms to customize the level of engagement in the revision, among other aspects. To analyze the potential and practicability of our proposal, we conduct a controlled experiment in which 39 participants, including academics, professional developers, and student collaborators, interact with InterEvo-TR. Our results show that the strategy to select and present intermediate results is effective for the purpose of readability assessment. Furthermore, the participants' actions and responses to a questionnaire allowed us to analyze the aspects influencing test code readability and the benefits and limitations of an interactive approach in the context of test case generation, paving the way for future developments based on interactivity.

关键词： Test pattern generators Search problems Optimization Codes Task analysis Statistics Sociology evolutionary computing and genetic algorithms interactive search-based software engineering readability testing tools

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Position-Relative Identities in the Internet of Things: An evolutionary GHT Approach

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IEEE INTERNET OF THINGS JOURNAL 2014年第5期1卷 497-507页

作者： Attwood, Andrew Lamb, David J. Abuelmaatti, Omar Manchester Metropolitan Univ Sch Comp Math & Digital Technol Manchester M1 5GD Lancs England Liverpool John Moores Univ Sch Comp & Math Sci Liverpool L3 3AF Merseyside England

The Internet of Things (IoT) will result in the deployment of many billions of wireless embedded systems creating interactive pervasive environments. It is envisaged that devices will cooperate to provide greater system knowledge than the sum of its parts. In an emergency situation, the flow of data across the IoT may be disrupted, giving rise to a requirement for machine-to-machine interaction within the remaining ubiquitous environment. Geographic hash tables (GHTs) provide an efficient mechanism to support fault-tolerant rendezvous communication between devices. However, current approaches either rely on devices being equipped with a GPS or being manually assigned an identity. This is unrealistic when the majority of these systems will be located inside buildings and will be too numerous to expect manual configuration. Additionally, when using GHT as a distributed data store, imbalance in the topology can lead to storage and routing overhead. This causes unfair work load, exhausting limited power supplies as well as causing poor data redundancy. To deal with these issues, we propose an approach that balances graph-based layout identity assignment, through the application of multifitness genetic algorithms. Our experiments show through simulation that our multifitness evolution technique improves on the initial graph-based layout, providing devices with improved balance and reachability metrics.

关键词： Data-centric storage evolutionary computing and genetic algorithms information dispersal load balancing wireless sensor networks

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Self-Reconfigurable Evolvable Hardware System for Adaptive Image Processing

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IEEE TRANSACTIONS ON COMPUTERS 2013年第8期62卷 1481-1493页

作者： Salvador, Ruben Otero, Andres Mora, Javier de la Torre, Eduardo Riesgo, Teresa Sekanina, Lukas Univ Politecn Madrid Elect & Microelect Design Res Grp GDEM Res Ctr Software Technol & Multimedia Syst Sustai Dept Sistemas Elect & ControlEUIT Telecomunicac Madrid 28031 Spain Univ Politecn Madrid Ctr Elect Ind ETSI Ind E-28006 Madrid Spain Brno Univ Technol Fac Informat Technol IT4Innovat Ctr Excellence Brno 61266 Czech Republic

This paper presents an evolvable hardware system, fully contained in an FPGA, which is capable of autonomously generating digital processing circuits, implemented on an array of processing elements (PEs). Candidate circuits are generated by an embedded evolutionary algorithm and implemented by means of dynamic partial reconfiguration, enabling evaluation in the final hardware. The PE array follows a systolic approach, and PEs do not contain extra logic such as path multiplexers or unused logic, so array performance is high. Hardware evaluation in the target device and the fast reconfiguration engine used yield smaller reconfiguration than evaluation times. This means that the complete evaluation cycle is faster than software-based approaches and previous evolvable digital systems. The selected application is digital image filtering and edge detection. The evolved filters yield better quality than classic linear and nonlinear filters using mean absolute error as standard comparison metric. Results do not only show better circuit adaptation to different noise types and intensities, but also a nondegrading filtering behavior. This means they may be run iteratively to enhance filtering quality. These properties are even kept for high noise levels (40 percent). The system as a whole is a step toward fully autonomous, adaptive systems.

关键词： Evolvable hardware FPGAs self-adaptive systems reconfigurable hardware adaptable architectures autonomous systems evolutionary computing and genetic algorithms

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An Automated Framework for Incorporating News into Stock Trading Strategies

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2014年第4期26卷 823-835页

作者： Nuij, Wijnand Milea, Viorel Hogenboom, Frederik Frasincar, Flavius Kaymak, Uzay Semlab NL-2408 ZE Alphen Aan Den Rijn Zuid Holland Netherlands Erasmus Univ Erasmus Sch Econ NL-3000 DR Rotterdam Netherlands Eindhoven Univ Technol Dept Ind Engn & Innovat Sci NL-5600 MB Eindhoven Netherlands

In this paper we present a framework for automatic exploitation of news in stock trading strategies. Events are extracted from news messages presented in free text without annotations. We test the introduced framework by deriving trading strategies based on technical indicators and impacts of the extracted events. The strategies take the form of rules that combine technical trading indicators with a news variable, and are revealed through the use of genetic programming. We find that the news variable is often included in the optimal trading rules, indicating the added value of news for predictive purposes and validating our proposed framework for automatically incorporating news in stock trading strategies.

关键词： Computer applications evolutionary computing and genetic algorithms learning natural language processing web text analysis

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The GA and the GWAS: Using genetic algorithms to Search for Multilocus Associations

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第3期9卷 899-910页

作者： Mooney, Michael A. Wilmot, Beth McWeeney, Shannon K. Oregon Hlth & Sci Univ Dept Med Informat & Clin Epidemiol Portland OR 97239 USA

Enormous data collection efforts and improvements in technology have made large genome-wide association studies a promising approach for better understanding the genetics of common diseases. Still, the knowledge gained from these studies may be extended even further by testing the hypothesis that genetic susceptibility is due to the combined effect of multiple variants or interactions between variants. Here, we explore and evaluate the use of a genetic algorithm to discover groups of SNPs (of size 2, 3, or 4) that are jointly associated with bipolar disorder. The algorithm is guided by the structure of a gene interaction network, and is able to find groups of SNPs that are strongly associated with the disease, while performing far fewer statistical tests than other methods.

关键词： Biology and genetics evolutionary computing and genetic algorithms graphs and networks

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Multi-Container Migration Strategy Optimization for Industrial Robotics Workflow Based on Hybrid Tabu-evolutionary Algorithm

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IEEE TRANSACTIONS ON SERVICES computing 2024年第5期17卷 2640-2653页

作者： Xie, Xingju Wu, Xiaojun Hu, Qiao Yuan, Sheng Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Shaanxi Key Lab Intelligent Robots Xian 710049 Peoples R China

Industrial Robot Monitoring System (IRMS) is an important guarantee to maintain the normal operation of industrial robot systems. For IRMSs in the edge-cloud environment, live migration technology enables them to improve system resource utilization and reliability such as dynamic resource management or fault tolerance without interrupting monitoring services. Therefore, it is important to research the optimization of live migration for IRMS. For multi-container migration, parallel migration can reduce service downtime, serial migration can reduce pre-copy migration time, and hybrid migration with a reasonable serial-parallel relationship can combine the advantages of both. In this paper, we propose a multi-container migration architecture based on shared bandwidth, which considers the resource-constrained characteristics of the edge-cloud environment. Moreover, we present a multi-container hybrid migration planning model with the total migration time as the optimization objective, which uses a matrix representation of serial-parallel relationship. To solve this model, we develop a heuristic algorithm based on a hybrid Tabu-evolutionary algorithm. The algorithm can find the dominant solution quickly by global search and improve the solution quality by subspace search. The experimental results show that the proposed algorithm can quickly give the hybrid migration strategy for a set of containers, effectively reducing the total migration time.

关键词： Containers Monitoring Bandwidth Cloud computing Computer architecture Optimization Industrial robots container migration edge computing evolutionary computing and genetic algorithms hybrid migration industrial robot monitoring system live migration optimization scheduling

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A Novel evolutionary Approach for Adaptive Random Testing

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IEEE TRANSACTIONS ON RELIABILITY 2009年第4期58卷 619-633页

作者： Tappenden, Andrew F. Miller, James Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

Random testing is a low cost strategy that can be applied to a wide range of testing problems. While the cost and straightforward application of random testing are appealing, these benefits must be evaluated against the reduced effectiveness due to the generality of the approach. Recently, a number of novel techniques, coined Adaptive Random Testing, have sought to increase the effectiveness of random testing by attempting to maximize the testing coverage of the input domain. This paper presents the novel application of an evolutionary search algorithm to this problem. The results of an extensive simulation study are presented in which the evolutionary approach is compared against the Fixed Size Candidate Set (FSCS), Restricted Random Testing (RRT), quasi-random testing using the Sobol sequence (Sobol), and random testing (RT) methods. The evolutionary approach was found to be superior to FSCS, RRT, Sobol, and RT amongst block patterns, the arena in which FSCS, and RRT have demonstrated the most appreciable gains in testing effectiveness. The results among fault patterns with increased complexity were shown to be similar to those of FSCS, and RRT;and showed a modest improvement over Sobol, and RT. A comparison of the asymptotic and empirical runtimes of the evolutionary search algorithm, and the other testing approaches, was also considered, providing further evidence that the application of an evolutionary search algorithm is feasible, and within the same order of time complexity as the other adaptive random testing approaches.

关键词： Adaptive random testing automated testing evolutionary computing and genetic algorithms random testing software testing test generation test strategies

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Collaborative Research Centre 531: Computational Intelligence - Theory and Practice

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IT-INFORMATION TECHNOLOGY 2007年第1期49卷 49-57页

作者： Mehnen, Joern Michelitsch, Thomas Witt, Carsten Univ Dortmund Dortmund Germany Univ Dortmund Dept Machining Technol Dortmund Germany Cranfield Univ Cranfield Beds England Cranfield Univ Decis Engn Ctr Cranfield Beds England Univ Dortmund Dept Comp Sci D-44221 Dortmund Germany

Computational Intelligence (CI) is an umbrella term for modern problem solvers such as evolutionary algorithms, Neural Networks and Fuzzy Logic. These methods have received increasing attention due to their simplicity, robustness and generality. The Collaborative Research Center "Computational Intelligence" (SFB 531) is concerned with the theoretical foundations and applications of CI methods. This article focuses on two examples from different research domains within SFB 531. First, exemplary results for the runtime analysis of evolutionary algorithms are summarized and evaluated. Second, applied research on mold temperature control is dealt with. Here it is stressed how the wide variety of CI methods leads to very efficient solutions to the problem and how still substantial improvements can be obtained by hybridization with expert's knowledge.

关键词： I.2.8 [Problem Solving Control Methods and Search] Heuristic Methods evolutionary computing and genetic algorithms

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Local Optimization Often is Ill-conditioned in genetic Programming for Symbolic Regression 24

Local Optimization Often is Ill-conditioned in Genetic Progr...

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24th International Symposium on Symbolic and Numeric algorithms for Scientific computing (SYNASC)

作者： Kronberger, Gabriel Univ Appl Sci Upper Austria Josef Ressel Ctr Symbol Regress Heurist & Evolutionary Algorithms Lab Hagenberg Campus Muhlkreis Austria

ISBN: (纸本)9781665465458

Gradient-based local optimization has been shown to improve results of genetic programming (GP) for symbolic regression. Several state-of-the-art GP implementations use iterative nonlinear least squares (NLS) algorithms such as the Levenberg-Marquardt algorithm for local optimization. The effectiveness of NLS algorithms depends on appropriate scaling and conditioning of the optimization problem. This has so far been ignored in symbolic regression and GP literature. In this study we use a singular value decomposition of NLS Jacobian matrices to determine the numeric rank and the condition number. We perform experiments with a GP implementation and six different benchmark datasets. Our results show that rank-deficient and ill-conditioned Jacobian matrices occur frequently and for all datasets. The issue is less extreme when restricting GP tree size and when using many non-linear functions in the function set.

关键词： evolutionary computing and genetic algorithms Nonlinear approximation Least squares methods Gradient methods

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Integrating Agent Actions with genetic Action Sequence Method 19

Integrating Agent Actions with Genetic Action Sequence Metho...

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genetic and evolutionary Computation Conference (GECCO)

作者： Kim, Man-Je Kim, Jun Suk Lee, Donghyeon Kim, Sungjin James Kim, Min-Jung Ahn, Chang Wook Gwangju Inst Sci & Technol Gwangju South Korea LG Elect Seoul South Korea

ISBN: (纸本)9781450367486

Reinforcement learning in general is suitable for putting actions in a specific order within a short sequence, but in the long run its greedy nature leads to eventual incompetence. This paper presents a brief description and implementative analysis of Action Sequence which was designed to deal with such a "penny-wise and pound-foolish" problem. Based on a combination of genetic operations and Monte-Carlo tree search, our proposed method is expected to show improved computational efficiency especially on problems with high complexity, in which situational difficulties are often troublesome to resolve with naive behaviors. We tested the method on a video game environment to validate its overall performance.

关键词： Artificial Intelligence evolutionary computing and genetic algorithms Video Game

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