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Journal articles on the topic 'Mutation operators'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Lim, Siew Mooi, Abu Bakar Md Sultan, Md Nasir Sulaiman, Aida Mustapha, and K. Y. Leong. "Crossover and Mutation Operators of Genetic Algorithms." International Journal of Machine Learning and Computing 7, no. 1 (2017): 9–12. http://dx.doi.org/10.18178/ijmlc.2017.7.1.611.

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2

Gupta, Pranshu. "Formalization of Mutation Operators." Journal of Computer Science Applications and Information Technology 3, no. 1 (2018): 1–6. http://dx.doi.org/10.15226/2474-9257/3/1/00125.

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3

Abraham, R., and M. Erwig. "Mutation Operators for Spreadsheets." IEEE Transactions on Software Engineering 35, no. 1 (2009): 94–108. http://dx.doi.org/10.1109/tse.2008.73.

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4

Gómez-Abajo, Pablo, Esther Guerra, Juan de Lara, and Mercedes G. Merayo. "Systematic Engineering of Mutation Operators." Journal of Object Technology 19, no. 3 (2020): 3:1. http://dx.doi.org/10.5381/jot.2020.19.3.a5.

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5

Ferrolho, António, and Manuel Crisóstomo. "Optimization of Genetic Operators for Scheduling Problems." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (2007): 1092–98. http://dx.doi.org/10.20965/jaciii.2007.p1092.

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Genetic algorithms (GA) can provide good solutions for scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.

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6

Strug, Joanna. "Classification of Mutation Operators Applied to Design Models." Key Engineering Materials 572 (September 2013): 539–42. http://dx.doi.org/10.4028/www.scientific.net/kem.572.539.

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Mutation testing is a testing technique supporting assessment of tests quality or selection of adequate tests. The technique, to perform effectively, requires a set of so called mutation operators to be defined. However, these operators may have different impact on the process, costs and results of mutation testing. Thus, a choice of operators that are most likely to ensure accurate results at acceptable costs is essential to make mutation testing practical. The paper presents results of an experiment evaluating effectiveness of operators defined for design models. The results of the experimen

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7

Deng, Lin, and Jeff Offutt. "Experimental Evaluation of Redundancy in Android Mutation Testing." International Journal of Software Engineering and Knowledge Engineering 28, no. 11n12 (2018): 1597–618. http://dx.doi.org/10.1142/s0218194018400193.

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Because of the widespread usage of Android devices, the Android ecosystem has the highest numbers of users, developers, and app downloads. Researchers find that many Android apps are not sufficiently tested, which may lead to crashes, incorrect behaviors, and security vulnerabilities. Mutation testing is a syntax-based software testing technique that is very effective at designing high-quality tests and evaluating pre-existing tests. Our prior research designed and implemented Android mutation testing technique, and then used experiments to assess its strength. However, the high computational

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8

Ullah, Sami, Abdus Salam, and Mohsin Masood. "Analysis and comparison of a proposed mutation operator and its effects on the performance of genetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1208–16. https://doi.org/10.11591/ijeecs.v25.i2.pp1208-1216.

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Genetic algorithms (GAs) are dependent on various operators and parameters. The most common evolutionary operators are parent selection, crossover, and mutation. Each operator has broad implementations with its pros and cons. A successful GA is highly dependent on genetic diversity which is the main driving force that steers a GA towards an optimal solution. Mutation operator implements the idea of exploration to search for uncharted areas and introduces diversity in a population. Thus, increasing the probability of GA to converge to a globally optimum solution. In this paper, a new variant of

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9

Esra'a Alkafaween and Ahmad B. A. Hassanat. "Improving TSP Solutions Using GA with a New Hybrid Mutation Based on Knowledge and Randomness." Communications - Scientific letters of the University of Zilina 22, no. 3 (2020): 128–39. http://dx.doi.org/10.26552/com.c.2020.3.128-139.

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Genetic algorithm (GA) is an efficient tool for solving optimization problems by evolving solutions, as it mimics the Darwinian theory of natural evolution. The mutation operator is one of the key success factors in GA, as it is considered the exploration operator of GA. Various mutation operators exist to solve hard combinatorial problems such as the TSP. In this paper, we propose a hybrid mutation operator called "IRGIBNNM", this mutation is a combination of two existing mutations; a knowledgebased mutation, and a random-based mutation. We also improve the existing “select best mutatio

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10

Chen, Luoyun, and Weiwei Wang. "Online English Teaching System under the Background of Epidemic Situation Based on Intelligent Feature Recognition Technology." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–12. http://dx.doi.org/10.1155/2022/6569279.

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In order to improve the effect of online English teaching in the context of the epidemic, this paper combines intelligent feature recognition technology to carry out an online English teaching system in the context of the epidemic and greatly reduces the number of variants by selecting mutation operators with excellent performance. Moreover, in this paper, the mutation adequacy and the number of mutation operators are regarded as two objective functions, and the selection problem of mutation operators is generated into a two-stage optimization problem, and the above problems are solved by a ge

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11

Suguna Mallika S. and Rajya Lakshmi D. "Mutation Testing and Its Analysis on Web Applications for Defect Prevention and Performance Improvement." International Journal of e-Collaboration 17, no. 1 (2021): 71–88. http://dx.doi.org/10.4018/ijec.2021010105.

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The society's increasing reliance on web applications with the growing online market and digitization of almost every service, there is an increasing demand for better reliability, security, and interoperability of web applications. Testing becomes an integral part of improving this reliability on web applications. Despite the innumerable number of tools, techniques, methods for testing web applications, there is still scope for expansion in the code coverage of web applications. Mutation testing with its expansive potential to expose vulnerabilities of web applications took a backseat owing t

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12

Alvarado, Suilen H. "Design of Mutation Operators for Testing Geographic Information Systems." Proceedings 21, no. 1 (2019): 43. http://dx.doi.org/10.3390/proceedings2019021043.

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In this article, we propose the definition of specific mutation operators for testing Geographic Information Systems. We describe the process for applying the operators and generating mutants, and present a case study where these mutation operators are applied to two real-world applications.

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13

Ma, Yu-Seung, Yong Rae Kwon, and Sang-Woon Kim. "Statistical Investigation on Class Mutation Operators." ETRI Journal 31, no. 2 (2009): 140–50. http://dx.doi.org/10.4218/etrij.09.0108.0356.

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14

Chellapilla, K. "Combining mutation operators in evolutionary programming." IEEE Transactions on Evolutionary Computation 2, no. 3 (1998): 91–96. http://dx.doi.org/10.1109/4235.735431.

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15

Deng, Lin, Jeff Offutt, Paul Ammann, and Nariman Mirzaei. "Mutation operators for testing Android apps." Information and Software Technology 81 (January 2017): 154–68. http://dx.doi.org/10.1016/j.infsof.2016.04.012.

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16

Gutiérrez-Madroñal, L., I. Medina-Bulo, and J. J. Domínguez-Jiménez. "Evaluation of EPL mutation operators with the MuEPL mutation system." Expert Systems with Applications 116 (February 2019): 78–95. http://dx.doi.org/10.1016/j.eswa.2018.09.003.

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17

Lewowski, Tomasz, and Lech Madeyski. "Mutants as Patches: Towards a formal approach to Mutation Testing." Foundations of Computing and Decision Sciences 44, no. 4 (2019): 379–405. http://dx.doi.org/10.2478/fcds-2019-0019.

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Abstract Background: Mutation testing is a widely explored technique used to evaluate the quality of software tests, but little attention has been given to its mathematical foundations. Aim: We provide a formal description of the core concepts in mutation testing, relations between them and conclusions that can be drawn from the presented model. Method: We introduce concepts of mutant space and patch space, and refer to patch merging procedure from the patch theory. We explicitly present constraints, such as location-dependence, that affect mutation operators. We also present a way to use intr

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18

Bondarenko, Oleksiy, Oleksandr Ustynenko, Roman Protasov, and Oleksandr Arkhipov. "CROSSOVER AND MUTATION OPERATORS IN STOCHASTIC ALGORITHMS." Bulletin of the National Technical University «KhPI» Series: Engineering and CAD, no. 1 (December 28, 2024): 3–9. https://doi.org/10.20998/2079-0775.2024.1.01.

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The relevance of illuminating contemporary stochastic algorithms is highlighted, with the past two decades witnessing a rapid development in stochastic algorithms, attributed to increased research capabilities and growing data volumes. These algorithms prove effective in solving complex optimization problems, garnering attention from the global scientific community and practitioners worldwide. An exploration of the role and an overview of key crossover and mutation operators in stochastic algorithms represent a pertinent scientific and practical endeavor, fostering deeper understanding and pop

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19

Ullah, Sami, Abdus Salam, and Mohsin Masood. "Analysis and comparison of a proposed mutation operator and its effects on the performance of genetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1208. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1208-1216.

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Genetic algorithms (GAs) are dependent on various operators and parameters. The most common evolutionary operators are parent selection, crossover, and mutation. Each operator has broad implementations with its pros and cons. A successful GA is highly dependent on genetic diversity which is the main driving force that steers a GA towards an optimal solution. Mutation operator implements the idea of exploration to search for uncharted areas and introduces diversity in a population. Thus, increasing the probability of GA to converge to a globally optimum solution. In this paper, a new variant of

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20

Li-Chao Feng, Li-Chao Feng, Xing-Ya Wang Li-Chao Feng, Shi-Yu Zhang Xing-Ya Wang, Rui-Zhi Gao Shi-Yu Zhang, and Zhi-Hong Zhao Rui-Zhi Gao. "Mutation Operator Reduction for Cost-effective Deep Learning Software Testing via Decision Boundary Change Measurement." 網際網路技術學刊 23, no. 3 (2022): 601–10. http://dx.doi.org/10.53106/160792642022052303018.

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<p>Mutation testing has been deemed an effective way to ensure Deep Learning (DL) software quality. Due to the requirements of generating and executing mass mutants, mutation testing suffers low-efficiency problems. In regard to traditional software, mutation operators that are hard to cause program logic changes can be reduced. Thus, the number of the mutants, as well as their executions, can be effectively decreased. However, DL software relies on model logic to make a decision. Decision boundaries characterize its logic. In this paper, we propose a DL software mutation operator reduct

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21

Sow, Babacar, Rodolphe Le Riche, Julien Pelamatti, Merlin Keller, and Sanaa Zannane. "Wasserstein-Based Evolutionary Operators for Optimizing Sets of Points: Application to Wind-Farm Layout Design." Applied Sciences 14, no. 17 (2024): 7916. http://dx.doi.org/10.3390/app14177916.

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This paper introduces an evolutionary algorithm for objective functions defined over clouds of points of varying sizes. Such design variables are modeled as uniform discrete measures with finite support and the crossover and mutation operators of the algorithm are defined using the Wasserstein barycenter. We prove that the Wasserstein-based crossover has a contracting property in the sense that the support of the generated measure is included in the closed convex hull of the union of the two parents’ supports. We introduce boundary mutations to counteract this contraction. Variants of evolutio

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22

Wang, Xuming, and Xiaobing Yu. "Differential Evolution Algorithm with Three Mutation Operators for Global Optimization." Mathematics 12, no. 15 (2024): 2311. http://dx.doi.org/10.3390/math12152311.

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Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization problems, which limits the performance of the algorithm. In this paper, six commonly used mutation operators are divided into three categories according to their own features. A mutation pool is established based on the three categories. A parameter pool with three predefined values is designed. During evolution, three mutation operators are randomly ch

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23

JIANG, YING, SHAN-SHAN HOU, JIN-HUI SHAN, LU ZHANG, and BING XIE. "AN APPROACH TO TESTING BLACK-BOX COMPONENTS USING CONTRACT-BASED MUTATION." International Journal of Software Engineering and Knowledge Engineering 18, no. 01 (2008): 93–117. http://dx.doi.org/10.1142/s0218194008003556.

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Component Based Software Development (CBSD) is gaining popularity in recent years. In this way of software development, software components, which are typically black-box components, are intensively reused to construct new systems. To ensure the quality of software systems composed of black-box components, a primary concern is how to ensure the quality of black-box components. Thus, adequate testing of those black-box components that will be reused is a necessary step in CBSD. However, due to the unavailability of the source code of black-box components, ensuring test adequacy becomes one of t

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24

Jansen, Thomas, and Dirk Sudholt. "Analysis of an Asymmetric Mutation Operator." Evolutionary Computation 18, no. 1 (2010): 1–26. http://dx.doi.org/10.1162/evco.2010.18.1.18101.

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Evolutionary algorithms are general randomized search heuristics and typically perform an unbiased random search that is guided only by the fitness of the search points encountered. However, in applications there is often problem-specific knowledge that suggests some additional bias. The use of appropriately biased variation operators may speed up the search considerably. Problems defined over bit strings of finite length often have the property that good solutions have only very few 1-bits or very few 0-bits. A mutation operator tailored toward such situations is studied under different persp

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25

Mresa, Elfurjani S., and Leonardo Bottaci. "Efficiency of mutation operators and selective mutation strategies: an empirical study." Software Testing, Verification and Reliability 9, no. 4 (1999): 205–32. http://dx.doi.org/10.1002/(sici)1099-1689(199912)9:4<205::aid-stvr186>3.0.co;2-x.

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26

Delgado-Pérez, Pedro, Sergio Segura, and Inmaculada Medina-Bulo. "Assessment of C++ object-oriented mutation operators: A selective mutation approach." Software Testing, Verification and Reliability 27, no. 4-5 (2017): e1630. http://dx.doi.org/10.1002/stvr.1630.

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27

Hong, Libin, Chenjian Liu, Jiadong Cui, and Fuchang Liu. "Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming." Discrete Dynamics in Nature and Society 2021 (October 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/1336929.

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Evolutionary programming (EP) uses a mutation as a unique operator. Gaussian, Cauchy, Lévy, and double exponential probability distributions and single-point mutation were nominated as mutation operators. Many mutation strategies have been proposed over the last two decades. The most recent EP variant was proposed using a step-size-based self-adaptive mutation operator. In SSEP, the mutation type with its parameters is selected based on the step size, which differs from generation to generation. Several principles for choosing proper parameters have been proposed; however, SSEP still has limit

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28

Lin, Mingmin, Yingpei Zeng, Ting Wu, Qiuhua Wang, Linan Fang, and Shanqing Guo. "GSA-Fuzz: Optimize Seed Mutation with Gravitational Search Algorithm." Security and Communication Networks 2022 (July 15, 2022): 1–17. http://dx.doi.org/10.1155/2022/1505842.

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Mutation-based fuzzing is currently one of the most effective techniques to discover software vulnerabilities. It relies on mutation strategies to generate interesting seeds. As a state-of-the-art mutation-based fuzzer, AFL follows a mutation strategy with high randomization, which uses randomly selected mutation operators to mutate seeds at random offsets. Its strategy may ignore some efficient mutation operators and mutation positions. Therefore, in this paper, we propose a solution named GSA-Fuzz to improve the efficiency of seed mutation strategy with the gravitational search algorithm (GS

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29

Saifan, Ahmad A., and Ahmad Adnan Alzyoud. "Mutation Testing to Evaluate Android Applications." International Journal of Open Source Software and Processes 11, no. 1 (2020): 23–40. http://dx.doi.org/10.4018/ijossp.2020010102.

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Android is an operating system source which offers flexibility and support for most mobile applications, and easy access to social networks. It is important to understand the complexity of design, development, implementation, and testing of Android apps. A number of challenges may be faced in testing android applications, including the lack of testing processes and methods, testing experts being unavailable, poor in-house testing environment, and time restrictions. Mutation testing is a fault-based testing technique, applied by generating mutants and running the application with these mutants

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30

Gupta, R., C. Verma, and N. Singh . "Mutation Operators in Python using SMT-P." International Journal of Computer Sciences and Engineering 6, no. 7 (2018): 776–78. http://dx.doi.org/10.26438/ijcse/v6i7.776778.

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31

Raidl, G. R., G. Koller, and B. A. Julstrom. "Biased mutation operators for subgraph-selection problems." IEEE Transactions on Evolutionary Computation 10, no. 2 (2006): 145–56. http://dx.doi.org/10.1109/tevc.2006.871251.

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32

Gong, Wenyin, and Zhihua Cai. "Differential Evolution With Ranking-Based Mutation Operators." IEEE Transactions on Cybernetics 43, no. 6 (2013): 2066–81. http://dx.doi.org/10.1109/tcyb.2013.2239988.

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33

Banzi, Adam S., Tiago Nobre, Gabriel B. Pinheiro, João Carlos G. Árias, Aurora Pozo, and Silvia Regina Vergilio. "Selecting mutation operators with a multiobjective approach." Expert Systems with Applications 39, no. 15 (2012): 12131–42. http://dx.doi.org/10.1016/j.eswa.2012.04.041.

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34

Woodward, M. R. "Concerning ordered mutation testing of relational operators." Software Testing, Verification and Reliability 1, no. 3 (1991): 35–40. http://dx.doi.org/10.1002/stvr.4370010305.

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35

Satman, Mehmet Hakan, and Emre Akadal. "Machine-coded genetic operators and their performances in floating-point genetic algorithms." International Journal of Advanced Mathematical Sciences 5, no. 1 (2017): 8. http://dx.doi.org/10.14419/ijams.v5i1.7128.

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Machine-coded genetic algorithms (MCGAs) use the byte representation of floating-point numbers which are encoded in the computer memory. Use of the byte alphabet makes classical crossover operators directly applicable in the floating-point genetic algorithms. Since effect of the byte-based mutation operator depends on the location of the mutated byte, the byte-based mutation operator mimics the functionality of its binary counterpart. In this paper, we extend the MCGA by developing new type of byte-based genetic operators including a random mutation and a random dynamic mutation operator. We p

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36

Hou, Wei, HongBin Dong, and GuiSheng Yin. "Co-Evolutionary Algorithms Based on Mixed Strategy." Journal of Information Technology Research 4, no. 2 (2011): 17–30. http://dx.doi.org/10.4018/jitr.2011040102.

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Inspired by evolutionary game theory, this paper modifies previous mixed strategy framework, adding a new mutation operator and extending to crossover operation, and proposes co-evolutionary algorithms based on mixed crossover and/or mutation strategy. The mixed mutation strategy set consists of Gaussian, Cauchy, Levy, single point and differential mutation operators; the mixed crossover strategy set consists of cuboid, two-points and heuristic crossover operators. The novel algorithms automatically select crossover and/or mutation operators from a given mixed strategy set, and improve the evo

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37

Ngambusabongsopa, Ransikarn, Zhiyong Li, and Esraa Eldesouky. "A Hybrid Mutation Chemical Reaction Optimization Algorithm for Global Numerical Optimization." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/375902.

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This paper proposes a hybrid metaheuristic approach that improves global numerical optimization by increasing optimal quality and accelerating convergence. This algorithm involves a recently developed process for chemical reaction optimization and two adjustment operators (turning and mutation operators). Three types of mutation operators (uniform, nonuniform, and polynomial) were combined with chemical reaction optimization and turning operator to find the most appropriate framework. The best solution among these three options was selected to be a hybrid mutation chemical reaction optimizatio

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38

Jalal-ud-Din, Ehtasham-ul-Haq, Ibrahim M. Almanjahie, and Ishfaq Ahmad. "Enhancing probabilistic based real-coded crossover genetic algorithms with authentication of VIKOR multi-criteria optimization method." AIMS Mathematics 9, no. 10 (2024): 29250–68. http://dx.doi.org/10.3934/math.20241418.

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<p> To improve the performance of genetic algorithms (GAs) in complex optimization settings, this work offered two novel real-coded crossover operators: one based on the Gumbel distribution (GX) and the other on the Rayleigh distribution (RX). These innovative operators, when combined with three different mutation techniques, created a significant improvement in GA methodology. Our meticulous simulations showed that the GX operator significantly outperformed RX and other traditional operators, demonstrating its superior capacity to address complex optimization problems. The GX operator's

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39

Rani, Shweta, and Bharti Suri. "Investigating Different Metrics for Evaluation and Selection of Mutation Operators for Java." International Journal of Software Engineering and Knowledge Engineering 31, no. 03 (2021): 311–36. http://dx.doi.org/10.1142/s021819402150011x.

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Mutation testing is a successful and powerful technique, specifically designed for injecting the artificial faults. Although it is effective at revealing the faults, test suite assessment and its reduction, however, suffer from the expense of executing a large number of mutants. The researchers have proposed different types of cost reduction techniques in the literature. These techniques highly depend on the inspection of mutation operators. Several metrics have been evolved for the same. The selective mutation technique is most frequently used by the researchers. In this paper, the authors in

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Abbas, Sara Tarek ElSayed, Rohayanti Hassan, Shahliza Abd Halim, Shahreen Kasim, and Rohaizan Ramlan. "Investigation on Java Mutation Testing Tools." JOIV : International Journal on Informatics Visualization 6, no. 2-2 (2022): 455. http://dx.doi.org/10.30630/joiv.6.2-2.1090.

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Software Testing is one of the most significant phases within the software development life cycle since software bugs can be costly and traumatic. However, the traditional software testing process is not enough on its own as some undiscovered faults might still exist due to the test cases’ inability to detect all underlying faults. Amidst the various proposed techniques of test suites’ efficiency detection comes mutation testing, one of the most effective approaches as declared by many researchers. Nevertheless, there is not enough research on how well the mutation testing tools adhere to the

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41

Angelova, Maria, and Tania Pencheva. "Influence of Genetic Algorithm Parameters on Their Performance for Parameter Identification of a Yeast Fed-batch Fermentation Process Model." International Journal Bioautomation 28, no. 4 (2024): 233–44. https://doi.org/10.7546/ijba.2024.28.4.001038.

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Eight single (SGA) and eight multi-population (MGA) genetic algorithms (GA) differing in the sequence of implementation of the main genetic operators’ selection, crossover and mutation, or omitting the mutation operator, have been examined for the purposes of parameter identification of a Saccharomyces cerevisiae fed-batch fermentation process model. The influence of some of the main genetic algorithm parameters, namely number of individuals, maximum number of generations, generation gap, crossover and mutation rates for both SGA and MGA, and insertion and migration probability for MGA only, h

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42

Yin, Yong Feng, Yi Bin Zhou, and Yan Rong Wang. "Research and Improvements on Mutation Operators for Simulink Models." Applied Mechanics and Materials 687-691 (November 2014): 1389–93. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1389.

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In order to test the Simulink models, this paper focused on the model-based testing for Simulink based on mutation testing. Considering the situation that the present model-based test adequacy criteria are imperfect, we propose a mutation testing process for Simulink models. Based on studying the application of mutation testing technique to Simulink models, some improvements on mutation operators for Simulink models are presented. The experimentation results verified the effectiveness and correctness.

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43

Mustafa, Kaya. "Applying Developed Genetic Algorithm Operators to the Knapsack Problems." Global Journal of Computer Sciences: Theory and Research 13, no. 2 (2023): 75–91. http://dx.doi.org/10.18844/gjcs.v13i2.9192.

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This study investigated the effect of the previously developed random mixed crossover (RMC), back controlled selection (BCSO), double directions sensitive mutation operators (DDSM), and backward controlled termination criteria (BCTC) on the performance of a genetic algorithm (GA). In the first study, the following three benchmark 0-1, bounded, and unbounded knapsack problems problems were analyzed. In the first stage, the existing operators namely; multi-point crossover operator and tournament selection operator, 1% mutation ratio, and the fitness convergence termination criteria were applied

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44

Hassanat, Ahmad, Khalid Almohammadi, Esra’a Alkafaween, Eman Abunawas, Awni Hammouri, and V. B. Surya Prasath. "Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach." Information 10, no. 12 (2019): 390. http://dx.doi.org/10.3390/info10120390.

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Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine

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45

Konovalov, I. S., V. A. Fatkhi, and V. G. Kobak. "Genetic algorithm efficiency improvement in the course of set cover problem solution." Vestnik of Don State Technical University 19, no. 4 (2020): 389–97. http://dx.doi.org/10.23947/1992-5980-2019-19-4-389-397.

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Introduction. Practical tasks (location of service points, creation of microcircuits, scheduling, etc.) often require an exact or approximate to exact solution at a large dimension. In this case, achieving an acceptable result requires solving a set cover problem, fundamental for combinatorics and the set theory. An exact solution can be obtained using exhaustive methods; but in this case, when the dimension of the problem is increased, the time taken by an exact algorithm rises exponentially. For this reason, the precision of approximate methods should be increased: they give a solution that

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46

NARIMANI, ZAHRA, HAMID BEIGY, and HASSAN ABOLHASSANI. "A NEW GENETIC ALGORITHM FOR MULTIPLE SEQUENCE ALIGNMENT." International Journal of Computational Intelligence and Applications 11, no. 04 (2012): 1250023. http://dx.doi.org/10.1142/s146902681250023x.

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Multiple sequence alignment (MSA) is one of the basic and important problems in molecular biology. MSA can be used for different purposes including finding the conserved motifs and structurally important regions in protein sequences and determine evolutionary distance between sequences. Aligning several sequences cannot be done in polynomial time and therefore heuristic methods such as genetic algorithms can be used to find approximate solutions of MSA problems. Several algorithms based on genetic algorithms have been developed for this problem in recent years. Most of these algorithms use ver

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Delgado-Pérez, Pedro, Inmaculada Medina-Bulo, Francisco Palomo-Lozano, Antonio García-Domínguez, and Juan José Domínguez-Jiménez. "Assessment of class mutation operators for C++ with the MuCPP mutation system." Information and Software Technology 81 (January 2017): 169–84. http://dx.doi.org/10.1016/j.infsof.2016.07.002.

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Loh, Zheung Yik, Wan Mohd Nasir Wan Kadir, and Noraini Ibrahim. "A Comparative Evaluation of Transformers in Seq2Seq Code Mutation: Non-Pre-trained Vs. Pre-trained Variants." Journal of Advanced Research Design 123, no. 1 (2024): 45–65. https://doi.org/10.37934/ard.123.1.4565.

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Mutation testing (MT) is a gold standard way to assess the efficacy of software test suites. However, the accuracy of mutation score is affected by the presence of trivial mutants which can be “killed” by even the simplest and most basic test suites. Since the existence of trivial mutants is due to the fixed set of mutation operators that constraints the complexity of code mutations, state-of-the-art recurrent neural network (RNN) model is used for sequence-to-sequence (seq2seq) code mutation without relying on mutation operators. However, the quality of the produced mutants is affected by the

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SHAHRIAR, HOSSAIN, and MOHAMMAD ZULKERNINE. "ASSESSING TEST SUITES FOR BUFFER OVERFLOW VULNERABILITIES." International Journal of Software Engineering and Knowledge Engineering 20, no. 01 (2010): 73–101. http://dx.doi.org/10.1142/s0218194010004621.

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Over the last few years, numerous vulnerabilities have been reported in software, and successful exploitations of these vulnerabilities have resulted in severe consequences such as denial of services and application state corruptions. Researches have shown that effective quality assurance methods can prevent such consequences when applied during software (or applications) development processes. Software security testing is a popular assurance method in this direction. However, effective testing involves obtaining an effective test suite (or collection of test cases) that can reveal specific fa

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Ahmed, Zakir Hussain, Md Taizuddin Choudhary, and Ibrahim Al-Dayel. "Effects of crossover operator combined with mutation operator in genetic algorithms for the generalized travelling salesman problem." International Journal of Industrial Engineering Computations 15, no. 3 (2024): 627–44. http://dx.doi.org/10.5267/j.ijiec.2024.5.004.

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Here, we consider the generalized travelling salesman problem (GTSP), which is a generalization of the travelling salesman problem (TSP). This problem has several real-life applications. Since the problem is complex and NP-hard, solving this problem by exact methods is very difficult. Therefore, researchers have applied several heuristic algorithms to solve this problem. We propose the application of genetic algorithms (GAs) to obtain a solution. In the GA, three operators—selection, crossover, and mutation—are successively applied to a group of chromosomes to obtain a solution to an optimizat

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