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Test Data Generation for Mutation Testing Based on Markov Chain Usage Model and estimation of distribution algorithm

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第3期50卷 551-573页

作者： Wei, Changqing Yao, Xiangjuan Gong, Dunwei Liu, Huai China Univ Min & Technol Sch Math Xuzhou 221116 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic 3122 Australia

Mutation testing, a mainstream fault-based software testing technique, can mimic a wide variety of software faults by seeding them into the target program and resulting in the so-called mutants. Test data generated in mutation testing should be able to kill as many mutants as possible, hence guaranteeing a high fault-detection effectiveness of testing. Nevertheless, the test data generation can be very expensive, because mutation testing normally involves an extremely large number of mutants and some mutants are hard to kill. It is thus a critical yet challenging job to find an efficient way to generate a small set of test data that are able to kill multiple mutants at the same time as well as reveal those hard-to-detect faults. In this paper, we propose a new approach for test data generation in mutation testing, through the novel applications of the Markov chain usage model and the estimation of distribution algorithm. We first utilize the Markov chain usage model to reduce the so-called mutant branches in weak mutation testing and generate a minimal set of extended paths. Then, we regard the problem of generating test data as the problem of covering extended paths and use an estimation of distribution algorithm based on probability model to solve the problem. Finally, we develop a framework, TAMMEA, to implement the new approach of generating test data for mutation testing. The empirical studies based on fifteen object programs show that TAMMEA can kill more mutants using fewer test data compared with baseline techniques. In addition, the computation overhead of TAMMEA is lower than that of the baseline technique based on the traditional genetic algorithm, and comparable to that of the random method. It is clear that the new approach improves both the effectiveness and efficiency of mutation testing, thus promoting its practicability.

关键词： Testing Markov processes estimation Software algorithms Genetic algorithms Data models Costs Mutation testing weak mutation test data generation Markov chain usage model coverage of extended path estimation of distribution algorithm

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An estimation of distribution algorithm With Multi-Leader Search

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IEEE ACCESS 2020年 8卷 37383-37405页

作者： Wang, Xiaofei Han, Tong Zhao, Hui Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China

The estimation of distribution algorithm (EDA) is a well-known stochastic search method but is easily affected by the ill-shaped distribution of solutions and can thus become trapped in stagnation. In this paper, we propose a novel modified EDA with a multi-leader search (MLS) mechanism, namely, the MLS-EDA. To strengthen the exploration performance, an enhanced distribution model that considers the information of population and distribution is utilized to generate new candidates. Moreover, when the algorithm stagnates, the MLS mechanism will be activated to perform a local search and shrink the search scope. The performance of the MLS-EDA in addressing complex optimization problems is verified using the CEC 2014 and CEC 2017 testbeds with 30D, 50D and 100D tests. Several modern algorithms, including the top-performing methods in the CEC 2014 and CEC 2017 competitions, are considered as competitors. The competitive performance of our proposed MLS-EDA is discussed based on the comparison results.

关键词： estimation of distribution algorithm real-numerical optimization CEC 2014 CEC 2017 evolutionary computation

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Solution of a flexible jobshop scheduling problem using an estimation of distribution algorithm

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2015年第1期12卷 49-57页

作者： Perez, R. Joens, S. Hernandez, A. Ctr Innovac Aplicada Tecnol Competit CIATEC AC Posgrad Interinst Ciencia & Tecnol PICYT Leon 37545 Guanajuato Mexico CONACYT Benito Juarez 03940 DF Mexico Ctr Invest Matemat CIMAT AC Guanajuato 36240 Mexico

The flexible jobshop scheduling problem permits the operation of each job to be processed by more than one machine. The configuration mentioned generally seeks to minimize the completion time of all jobs known in the literature as 'makespan'. We propose an estimation of distribution algorithm for Sequencing, AEDS for simplicity and functionality. The AEDS attempts to find a relationship or interaction between the input variables, jobs, operations and shifts to optimize the output variable of real manufacturing processes, the makespan. In this sense the AEDS algorithm is used to guide the search and to solve the problem. In the algorithm, three graphical models were used to find better solutions. To set off-duty hours for operators before starting their activities in each shift as an input parameter and its development through the AEDS algorithm is a novelty of this research on the current research work. The comparison between AEDS and a genetic algorithm shows the effectiveness of AEDS solving the problem statement. Using the AEDS proposed, the performance Of real manufacturing processes can be improved significantly when different machines are assigned to different schedules.

关键词： Scheduling estimation of distribution algorithm

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estimation of distribution algorithm Based on Levy Flight for Solving the Set-Union Knapsack Problem

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IEEE ACCESS 2019年 7卷 132217-132227页

作者： Liu, Xue-Jing He, Yi-Chao Hebei GEO Univ Coll Informat & Engn Shijiazhuang 050031 Hebei Peoples R China

This article investigates how to use the estimation of distribution algorithm based on Levy flight to solve the set-union knapsack problem (SUKP). First, the mathematical model of the SUKP is introduced. Then, a quadratic greedy repair and optimization algorithm (Q-GROA), which deals with infeasible solutions, is proposed. Thereby, a new approach, the estimation of distribution algorithm based on Levy flight (LFEDA) combined with the Q-GROA is also proposed to solve the SUKP. A number of experiments are performed on the SUKP datasets to evaluate the performance of our proposed model. The results verify that the proposed method is significantly better than other algorithms with respect to the solution's performance.

关键词： Set-union knapsack problem estimation of distribution algorithm Levy flight repair and optimization

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An effective estimation of distribution algorithm for the flexible job-shop scheduling problem with fuzzy processing time

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2013年第12期51卷 3778-3793页

作者： Wang, Shengyao Wang, Ling Xu, Ye Liu, Min Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China

Considering the fuzzy nature of the data in real-world scheduling, an effective estimation of distribution algorithm (EDA) is proposed to solve the flexible job-shop scheduling problem with fuzzy processing time. A probability model is presented to describe the probability distribution of the solution space. A mechanism is provided to update the probability model with the elite individuals. By sampling the probability model, new individuals can be generated among the search region with promising solutions. Moreover, a left-shift scheme is employed for improving schedule solution when idle time exists on the machine. In addition, some fuzzy number operations are used to calculate scheduling objective value. The influence of parameter setting is investigated based on the Taguchi method of design of experiment, and a suitable parameter setting is suggested. Numerical testing results and comparisons with some existing algorithms are provided, which demonstrate the effectiveness of the proposed EDA.

关键词： flexible job-shop scheduling problem fuzzy processing time fuzzy completion time estimation of distribution algorithm probability model design of experiment

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An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2015年第3期28卷 307-320页

作者： Wang, Sheng-yao Wang, Ling Liu, Min Xu, Ye Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 10084 Peoples R China

In view of the stochastic nature of the data in real-world manufacturing systems, it is crucial to develop effective algorithms to solve the scheduling problems with uncertainty. In this paper, an order-based estimation of distribution algorithm (OEDA) is proposed to solve the hybrid flow-shop scheduling problem (HFSP) with stochastic processing times. Considering the effectiveness and robustness of a schedule, it aims to minimise the makespan of the initial scenario as well as the deviation of all results of the stochastic scenarios and the initial one. To be specific, a bi-objective function is used to evaluate the individuals of the population, and a probability model is designed to describe the probability distribution of the solution space. Meanwhile, optimal computing budget allocation (OCBA) technique is employed to provide a reliable identification to the good solutions among the population. A mechanism is also presented to update the probability model with the superior individuals that are identified by the OCBA. The new individuals are generated by sampling the probability model to track the area with promising solutions. In addition, the influence of parameter setting is investigated based on Taguchi method of design-of-experiment (DOE), and a suitable parameter setting is suggested. Extensive numerical testing results and comparisons with the existing algorithm are provided, which demonstrate the effectiveness of the proposed OEDA.

关键词： optimal computing budget allocation stochastic processing time estimation of distribution algorithm hybrid flow-shop scheduling problem robust scheduling

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Multiobjective estimation of distribution algorithm Based on Joint Modeling of Objectives and Variables

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2014年第4期18卷 519-542页

作者： Karshenas, Hossein Santana, Roberto Bielza, Concha Larranaga, Pedro Tech Univ Madrid Madrid 28660 Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp San Sebastin Donostia 20080 Spain

This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based on joint probabilistic modeling of objectives and variables. This EDA uses the multidimensional Bayesian network as its probabilistic model. In this way, it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learned between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm find better tradeoff solutions to the multiobjective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multiobjective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is first applied to the set of walking fish group problems, and its optimization performance is compared with a standard multiobjective evolutionary algorithm and another competitive multiobjective EDA. The experimental results show that on several of these problems, and for different objective space dimensions, the proposed algorithm performs significantly better and on some others achieves comparable results when compared with the other two algorithms. The algorithm is then tested on the set of CEC09 problems, where the results show that multiobjective optimization based on joint model estimation is able to obtain considerably better fronts for some of the problems compared with the search based on conventional genetic operators in the state-of-the-art multiobjective evolutionary algorithms.

关键词： estimation of distribution algorithm joint objective-variable modeling many-objective problem multiobjective optimization objectives relationship

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NMIEDA: estimation of distribution algorithm based on normalized mutual information

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第6期33卷 e6074-e6074页

作者： Lin, Zhiyi Su, Qing Xie, Guobo Guangdong Univ Technol Sch Comp Guangzhou Peoples R China

A new estimation of distribution algorithm based on normalized mutual information (NMIEDA) is proposed for overcoming the premature convergence of bivariate estimation of distribution algorithms. NMIEDA first uses normalized mutual information to measure the interaction between two variables and then generate a dependency forest model. Second, based on the concept of sporadic model building and a reward and punishment scheme in Selfish Gene, NMIEDA provides a new updating mechanism that accelerates the convergence speed. Finally, a new sampling mechanism is adopted in NMIEDA to improve the efficiency of sampling, which combines stochastic sampling, the opposition-based learning scheme and the mutation operator. The simulation results on benchmark problems and real-world problems demonstrate that NMIEDA often outperforms several other bivariate algorithms.

关键词： estimation of distribution algorithm new sampling mechanism new updating mechanism NMIEDA normalized mutual information

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An improved estimation of distribution algorithm for multi-objective optimization problems with mixed-variable

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NEURAL COMPUTING & APPLICATIONS 2022年第22期34卷 19703-19721页

作者： Wang, Wenxiang Li, Kangshun Jalil, Hassan Wang, Hui South China Agr Univ Coll Math & Informat Guangzhou 51006 Peoples R China Guangdong Univ Sci & Technol Sch Comp Sci Dongguan 523000 Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518172 Peoples R China

Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations;(2) using index coding for discrete variables to improve the search efficiency;and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity.

关键词： Multi-objective optimization Mixed-variable Evolutionary algorithm estimation of distribution algorithm Scalable histogram

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A hybrid differential evolution algorithm with estimation of distribution algorithm for reentrant hybrid flow shop scheduling problem

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NEURAL COMPUTING & APPLICATIONS 2018年第1期30卷 193-209页

作者： Zhou, Bing-hai Hu, Li-man Zhong, Zhen-yi Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China

This paper proposes a reentrant hybrid flow shop scheduling problem where inspection and repair operations are carried out as soon as a layer has completed fabrication. Firstly, a scheduling problem domain of reentrant hybrid flow shop is described, and then, a mathematical programming model is constructed with an objective of minimizing total weighted completion time. Then, a hybrid differential evolution (DE) algorithm with estimation of distribution algorithm using an ensemble model (eEDA), named DE-eEDA, is proposed to solve the problem. DE-eEDA incorporates the global statistical information collected from an ensemble probability model into DE. Finally, simulation experiments of different problem scales are carried out to analyze the proposed algorithm. Results indicate that the proposed algorithm can obtain satisfactory solutions within a short time.

关键词： Reentrant hybrid flow shop Scheduling Differential evolution algorithm estimation of distribution algorithm

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