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Journal articles on the topic 'Mutation Operator'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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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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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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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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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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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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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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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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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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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Kazakovtsev, Lev, Ivan Rozhnov, Guzel Shkaberina, and Viktor Orlov. "K-Means Genetic Algorithms with Greedy Genetic Operators." Mathematical Problems in Engineering 2020 (November 27, 2020): 1–16. http://dx.doi.org/10.1155/2020/8839763.

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The k-means problem is one of the most popular models of cluster analysis. The problem is NP-hard, and modern literature offers many competing heuristic approaches. Sometimes practical problems require obtaining such a result (albeit notExact), within the framework of the k-means model, which would be difficult to improve by known methods without a significant increase in the computation time or computational resources. In such cases, genetic algorithms with greedy agglomerative heuristic crossover operator might be a good choice. However, their computational complexity makes it difficult to u

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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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Ramos-Figueroa, Octavio, Marcela Quiroz-Castellanos, Efrén Mezura-Montes, and Nicadro Cruz-Ramírez. "An Experimental Study of Grouping Mutation Operators for the Unrelated Parallel-Machine Scheduling Problem." Mathematical and Computational Applications 28, no. 1 (2023): 6. http://dx.doi.org/10.3390/mca28010006.

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The Grouping Genetic Algorithm (GGA) is an extension to the standard Genetic Algorithm that uses a group-based representation scheme and variation operators that work at the group-level. This metaheuristic is one of the most used to solve combinatorial optimization grouping problems. Its optimization process consists of different components, although the crossover and mutation operators are the most recurrent. This article aims to highlight the impact that a well-designed operator can have on the final performance of a GGA. We present a comparative experimental study of different mutation oper

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Zou, Shiqi, Xiaoping Shi, and Shenmin Song. "MOEA with adaptive operator based on reinforcement learning for weapon target assignment." Electronic Research Archive 32, no. 3 (2024): 1498–532. http://dx.doi.org/10.3934/era.2024069.

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<abstract><p>Weapon target assignment (WTA) is a typical problem in the command and control of modern warfare. Despite the significance of the problem, traditional algorithms still have shortcomings in terms of efficiency, solution quality, and generalization. This paper presents a novel multi-objective evolutionary optimization algorithm (MOEA) that integrates a deep Q-network (DQN)-based adaptive mutation operator and a greedy-based crossover operator, designed to enhance the solution quality for the multi-objective WTA (MO-WTA). Our approach (NSGA-DRL) evolves NSGA-II by embeddi

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ul Haq, Ehtasham, Ishfaq Ahmad, and Ibrahim M. Almanjahie. "A Novel Parent Centric Crossover with the Log-Logistic Probabilistic Approach Using Multimodal Test Problems for Real-Coded Genetic Algorithms." Mathematical Problems in Engineering 2020 (October 31, 2020): 1–17. http://dx.doi.org/10.1155/2020/2874528.

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In this paper, a comprehensive empirical study is conducted to evaluate the performance of a new real-coded crossover operator called Fisk crossover (FX) operator. The basic aim of the proposed study is to preserve population diversity as well as to avoid local optima. In this context, a new crossover operator is designed and developed which is linked with Log-logistic probability distribution. For its global performance, a realistic comparison is made between FX versus double Pareto crossover (DPX), Laplace crossover (LX), and simulated binary crossover (SBX) operators. Moreover, these crosso

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Lin, WY, and KM Hsiao. "A new differential evolution algorithm with a combined mutation strategy for optimum synthesis of path-generating four-bar mechanisms." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 14 (2016): 2690–705. http://dx.doi.org/10.1177/0954406216638887.

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A one-phase synthesis method using heuristic optimization algorithms can solve the dimensional synthesis problems of path-generating four-bar mechanisms. However, due to the difficulty of the problem itself, there is still room for improvement in solution accuracy and reliability. Therefore, in this study, a new differential evolution (DE) algorithm with a combined mutation strategy, termed the combined-mutation differential evolution (CMDE) algorithm, is proposed to improve the solution quality. In the combined mutation strategy, the DE/best/1 operator and the DE/current-to-best/1 operator ar

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Kulikova, I. V. "Application of the variation operator in a genetic algorithm for the synthesis of fuzzy controllers." Herald of Dagestan State Technical University. Technical Sciences 47, no. 4 (2021): 92–100. http://dx.doi.org/10.21822/2073-6185-2020-47-4-92-100.

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Abstract. Objective. This article studies the problem of increasing the efficiency of fuzzy controller synthesis in a control system using a genetic algorithm. The best parameters of the fuzzy controller are selected using the crossing-over and mutation operators in the genetic algorithm. The operation of the mutation operator can lead to the formation of an incorrect set of parameters, which complicates the procedure for synthesizing a fuzzy controller.Methods. Arrays of parameter sets of membership functions, conclusions, and rule weights that are included in the fuzzy controller are compile

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CHEN, JIANYONG, QIUZHEN LIN, and QINGBIN HU. "APPLICATION OF NOVEL CLONAL ALGORITHM IN MULTIOBJECTIVE OPTIMIZATION." International Journal of Information Technology & Decision Making 09, no. 02 (2010): 239–66. http://dx.doi.org/10.1142/s0219622010003804.

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In this paper, a novel clonal algorithm applied in multiobjecitve optimization (NCMO) is presented, which is designed from the improvement of search operators, i.e. dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM) operator. The main notion of these approaches is to perform more coarse-grained search at initial stage in order to speed up the convergence toward the Pareto-optimal front. Once the solutions are getting close to the Pareto-optimal front, more fine-grained search is

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Serpell, Martin, and James E. Smith. "Self-Adaptation of Mutation Operator and Probability for Permutation Representations in Genetic Algorithms." Evolutionary Computation 18, no. 3 (2010): 491–514. http://dx.doi.org/10.1162/evco_a_00006.

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The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one “optimal choice”; rather, the situation changes per problem instance and during evolution. This paper examines whether this choice can be left to the processes of evolution via self-adaptation, thus removing this nontrivial task from the GA user and reducing the risk of poor performance arising from (inadvertent) inappropriate decisions. Sel

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Doerr, Benjamin, Nils Hebbinghaus, and Frank Neumann. "Speeding Up Evolutionary Algorithms through Asymmetric Mutation Operators." Evolutionary Computation 15, no. 4 (2007): 401–10. http://dx.doi.org/10.1162/evco.2007.15.4.401.

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Successful applications of evolutionary algorithms show that certain variation operators can lead to good solutions much faster than other ones. We examine this behavior observed in practice from a theoretical point of view and investigate the effect of an asymmetric mutation operator in evolutionary algorithms with respect to the runtime behavior. Considering the Eulerian cycle problem we present runtime bounds for evolutionary algorithms using an asymmetric operator which are much smaller than the best upper bounds for a more general one. In our analysis it turns out that a plateau which bot

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Hu, Zhongbo, Shengwu Xiong, Xiuhua Wang, Qinghua Su, Mianfang Liu, and Zhong Chen. "Subspace Clustering Mutation Operator for Developing Convergent Differential Evolution Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–18. http://dx.doi.org/10.1155/2014/154626.

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Many researches have identified that differential evolution algorithm (DE) is one of the most powerful stochastic real-parameter algorithms for global optimization problems. However, a stagnation problem still exists in DE variants. In order to overcome the disadvantage, two improvement ideas have gradually appeared recently. One is to combine multiple mutation operators for balancing the exploration and exploitation ability. The other is to develop convergent DE variants in theory for decreasing the occurrence probability of the stagnation. Given that, this paper proposes a subspace clusterin

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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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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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Jannoud, Ismael, Yousef Jaradat, Mohammad Z. Masoud, Ahmad Manasrah, and Mohammad Alia. "The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study." Electronics 11, no. 1 (2021): 28. http://dx.doi.org/10.3390/electronics11010028.

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A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and

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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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Assiri, Fatmah Yousef, and Asia Othman Aljahdali. "Software Vulnerability Fuzz Testing: A Mutation-Selection Optimization Systematic Review." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 14961–69. http://dx.doi.org/10.48084/etasr.6971.

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As software vulnerabilities can cause cybersecurity threats and have severe consequences, it is necessary to develop effective techniques to discover such vulnerabilities. Fuzzing is one of the most widely employed approaches that has been adapted for software testing. The mutation-based fuzzing approach is currently the most popular. The state-of-the-art American Fuzzy Lop (AFL) selects mutations randomly and lacks knowledge of mutation operations that are more helpful in a particular stage. This study performs a systematic review to identify and analyze existing approaches that optimize the

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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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Ohki, Makoto. "Periodic Mutation Operator for Nurse Scheduling by Using Cooperative GA." International Journal of Applied Evolutionary Computation 3, no. 3 (2012): 1–16. http://dx.doi.org/10.4018/jaec.2012070101.

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This paper proposes an effective mutation operator for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). NSP is a complex combinatorial optimizing problem for which many requirements must be considered. The changes of the shift schedule yields various problems, for example, a drop in the nursing level. The author describes a technique of the reoptimization of the nurse schedule in response to a change. CGA well suits local search, but its failure to handle global search leads to inferior solutions. CGA is superior in ability for local search by me

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Kim, Dae Won, Song Ko, and Bo Yeong Kang. "Estimation of Distribution Algorithms with Matrix Transpose in Bayesian Learning." Applied Mechanics and Materials 284-287 (January 2013): 3093–96. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3093.

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Estimation of distribution algorithms (EDAs) constitute a new branch of evolutionary optimization algorithms, providing effective and efficient optimization performance in a variety of research areas. Recent studies have proposed new EDAs that employ mutation operators in standard EDAs to increase the population diversity. We present a new mutation operator, a matrix transpose, specifically designed for Bayesian structure learning, and we evaluate its performance in Bayesian structure learning. The results indicate that EDAs with transpose mutation give markedly better performance than convent

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Zuevsky, Alexander. "Cluster algebras based on vertex operator algebras." International Journal of Modern Physics B 30, no. 28n29 (2016): 1640030. http://dx.doi.org/10.1142/s0217979216400300.

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Starting from Zhu recursion formulas for correlation functions for vertex operator algebras with formal parameters associated to local coordinates around marked points on a Riemann surfaces, we introduce a cluster algebra structure over a noncommutative set of variables. Cluster elements and mutation rules are explicitly defined. In particular, we propose an elliptic version of vertex operator cluster algebras arising from correlation functions and Zhu reduction procedure for vertex operators on the torus.

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Toklu, Y. Cengiz. "Application of genetic algorithms to construction scheduling with or without resource constraints." Canadian Journal of Civil Engineering 29, no. 3 (2002): 421–29. http://dx.doi.org/10.1139/l02-034.

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The difficulties encountered in scheduling construction projects with resource constraints are highlighted by means of a simplified bridge construction problem. A genetic algorithm applicable to projects with or without resource constraints is described. In this application, chromosomes are formed by genes consisting of the start days of the activities. This choice necessitated introducing two mathematical operators (datum operator and left compression operator) and emphasizing one genetic operator (fine mutation operator). A generalized evaluation of the fitness function is conducted. The alg

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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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Shem-Tov, Eliad, Moshe Sipper, and Achiya Elyasaf. "BERT Mutation: Deep Transformer Model for Masked Uniform Mutation in Genetic Programming." Mathematics 13, no. 5 (2025): 779. https://doi.org/10.3390/math13050779.

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We introduce BERT mutation, a novel, domain-independent mutation operator for Genetic Programming (GP) that leverages advanced Natural Language Processing (NLP) techniques to improve convergence, particularly using the Masked Language Modeling approach. By combining the capabilities of deep reinforcement learning and the BERT transformer architecture, BERT mutation intelligently suggests node replacements within GP trees to enhance their fitness. Unlike traditional stochastic mutation methods, BERT mutation adapts dynamically by using historical fitness data to optimize mutation decisions, res

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Stanovov, Vladimir, Shakhnaz Akhmedova, and Eugene Semenkin. "Difference-Based Mutation Operation for Neuroevolution of Augmented Topologies." Algorithms 14, no. 5 (2021): 127. http://dx.doi.org/10.3390/a14050127.

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In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for optimal weights and structure of the model. The difference is determined according to the innovation numbers assigned to each node and connection, allowing tracking the changes. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses a set of mutation operators, including connections merging and deleti

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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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Liagkouras, K., and K. Metaxiotis. "An Experimental Analysis of a New Interval-Based Mutation Operator." International Journal of Computational Intelligence and Applications 14, no. 03 (2015): 1550018. http://dx.doi.org/10.1142/s1469026815500182.

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In this paper, we present a novel Interval-Based Mutation (IBMU) operator. The proposed mutation operator is performing coarse-grained search at initial stage in order to speed up convergence toward more promising regions of the search landscape. Then, more fine-grained search is performed in order to guide the solutions towards the Pareto front. Computational experiments indicate that the proposed mutation operator performs better than conventional approaches for solving several well-known benchmarking problems.

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Calsina, Àngel, Sílvia Cuadrado, Laurent Desvillettes, and Gaël Raoul. "Asymptotics of steady states of a selection–mutation equation for small mutation rate." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 143, no. 6 (2013): 1123–46. http://dx.doi.org/10.1017/s0308210510001629.

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We consider a selection–mutation equation for the density of individuals with respect to a continuous phenotypic evolutionary trait. We assume that the competition term for an individual with a given trait depends on the traits of all the other individuals, therefore giving an infinite-dimensional nonlinearity. Mutations are modelled by means of an integral operator. We prove existence of steady states and show that, when the mutation rate goes to zero, the asymptotic profile of the population is a Cauchy distribution.

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FANG, WEI, JUN SUN, and WENBO XU. "ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM." New Mathematics and Natural Computation 05, no. 02 (2009): 487–96. http://dx.doi.org/10.1142/s179300570900143x.

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Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and i

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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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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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Corus, Dogan, Pietro S. Oliveto, and Donya Yazdani. "Fast Contiguous Somatic Hypermutations for Single-Objective Optimisation and Multi-Objective Optimisation Via Decomposition." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26922–30. https://doi.org/10.1609/aaai.v39i25.34897.

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Somatic Contiguous Hypermutations (CHM) are a popular variation operator used in artificial immune systems for optimisation tasks. Theoretical studies have shown that CHM operators can lead to considerable speed-ups in the expected optimisation time compared to the traditional standard bit mutation (SBM) operators used in evolutionary computation for both single-objective and multi-objective problems where it is advantageous to mutate large contiguous areas of the genotype representing the candidate solutions. These speed-ups can make the difference between polynomial and exponential runtimes,

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Fajfar, Iztok, and Tadej Tuma. "Creation of Numerical Constants in Robust Gene Expression Programming." Entropy 20, no. 10 (2018): 756. http://dx.doi.org/10.3390/e20100756.

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The problem of the creation of numerical constants has haunted the Genetic Programming (GP) community for a long time and is still considered one of the principal open research issues. Many problems tackled by GP include finding mathematical formulas, which often contain numerical constants. It is, however, a great challenge for GP to create highly accurate constants as their values are normally continuous, while GP is intrinsically suited for combinatorial optimization. The prevailing attempts to resolve this issue either employ separate real-valued local optimizers or special numeric mutatio

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Viana, Monique Simplicio, Orides Morandin Junior, and Rodrigo Colnago Contreras. "A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem." Sensors 20, no. 18 (2020): 5440. http://dx.doi.org/10.3390/s20185440.

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It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on loc

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Li, Shu Jie, Wei Jia Guo, and Zhi Gang Li. "Research of Dynamic Parameters Design on Genetic Algorithm." Applied Mechanics and Materials 263-266 (December 2012): 2282–86. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2282.

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To quickly get the global optimum by genetic algorithm, calculated the average and standard deviation of the adaptabilities of chromosomes of one generation, designed the selection operator and the mutation operator by the average and the standard deviation, dynamically adjusted the probability of selection operator and mutation operator and decreased the blindness of genetic algorithm, increased the multiplicity of chromosomes by multiple-point crossover and repetitiveness check. The test shows that the algorithm offered in the article is feasible and effective.

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Klimt, Martin, Jaromír Kukal, and Matej Mojzeš. "Lévy flights in binary optimization." Archives of Control Sciences 23, no. 4 (2013): 447–54. http://dx.doi.org/10.2478/acsc-2013-0027.

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Abstract There are many optimization heuristics which involves mutation operator. Reducing them to binary optimization allows to study properties of binary mutation operator. Modern heuristics yield from Lévy flights behavior, which is a bridge between local search and random shooting in binary space. The paper is oriented to statistical analysis of binary mutation with Lévy flight inside and Quantum Tunneling heuristics.

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Mansouri, Taha, Ahad Zare Ravasan, and Mohammad Reza Gholamian. "A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering." International Journal of Data Warehousing and Mining 10, no. 3 (2014): 1–14. http://dx.doi.org/10.4018/ijdwm.2014070101.

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One of the most widely used algorithms to solve clustering problems is the K-means. Despite of the algorithm's timely performance to find a fairly good solution, it shows some drawbacks like its dependence on initial conditions and trapping in local minima. This paper proposes a novel hybrid algorithm, comprised of K-means and a variation operator inspired by mutation in evolutionary algorithms, called Noisy K-means Algorithm (NKA). Previous research used K-means as one of the genetic operators in Genetic Algorithms. However, the proposed NKA is a kind of individual based algorithm that combin

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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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Purohit, Anuradha, Narendra S. Choudhari, and Aruna Tiwari. "A NEW MUTATION OPERATOR IN GENETIC PROGRAMMING." ICTACT Journal on Soft Computing 03, no. 02 (2013): 467–71. http://dx.doi.org/10.21917/ijsc.2013.0070.

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Prabha, Shashi, and Raghav Yadav. "Differential evolution with biological-based mutation operator." Engineering Science and Technology, an International Journal 23, no. 2 (2020): 253–63. http://dx.doi.org/10.1016/j.jestch.2019.05.012.

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Yuan, Gui Li, Yan Guang Xue, and Qing Jiao Liang. "The Design of Adaptive Immune Vaccine Algorithm." Advanced Materials Research 308-310 (August 2011): 1094–98. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1094.

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Aiming at disadvantages of Genetic Algorithm (GA) and learning from the immune system theory, this paper introduces immune memory cell of immune theory, vaccine extraction and vaccination operator based on immune theory, and adaptive probability crossover and mutation operator to GA, to improve the optimization ability and search efficiency of GA, and proposes Adaptive Immune Vaccine Algorithm (AIVA). Then proves the convergence of the algorithm, gives the composition mechanisms of the key operators, and verifies the role of each operator. Finally, four test functions have been optimized using

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Pérez-Vázquez, María, Carla López-Causapé, Andrés Corral-Lugo, et al. "Mutation Analysis in Regulator DNA-Binding Regions for Antimicrobial Efflux Pumps in 17,000 Pseudomonas aeruginosa Genomes." Microorganisms 11, no. 10 (2023): 2486. http://dx.doi.org/10.3390/microorganisms11102486.

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Mutations leading to upregulation of efflux pumps can produce multiple drug resistance in the pathogen Pseudomonas aeruginosa. Changes in their DNA binding regions, i.e., palindromic operators, can compromise pump depression and subsequently enhance resistance against several antibacterials and biocides. Here, we have identified (pseudo)palindromic repeats close to promoters of genes encoding 13 core drug-efflux pumps of P. aeruginosa. This framework was applied to detect mutations in these repeats in 17,292 genomes. Eighty-nine percent of isolates carried at least one mutation. Eight binary g

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