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Artykuły w czasopismach na temat „Genetic algorithms”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Sumida, Brian. "Genetics for genetic algorithms." ACM SIGBIO Newsletter 12, no. 2 (1992): 44–46. http://dx.doi.org/10.1145/130686.130694.

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Raol, Jitendra R., and Abhijit Jalisatgi. "From genetics to genetic algorithms." Resonance 1, no. 8 (1996): 43–54. http://dx.doi.org/10.1007/bf02837022.

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Nico, Nico, Novrido Charibaldi, and Yuli Fauziah. "Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center." International Journal of Artificial Intelligence & Robotics (IJAIR) 4, no. 1 (2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorit

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4

Babu, M. Nishidhar, Y. Kiran, and A. Ramesh V. Rajendra. "Tackling Real-Coded Genetic Algorithms." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 217–23. http://dx.doi.org/10.31142/ijtsrd5905.

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Srinath Murthy, Ahana, and Dattatreya P Mankame. "Genetic Algorithms - A Brief Study." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 1195–200. http://dx.doi.org/10.21275/sr24721004409.

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6

Abbas, Basim K. "Genetic Algorithms for Quadratic Equations." Aug-Sept 2023, no. 35 (August 26, 2023): 36–42. http://dx.doi.org/10.55529/jecnam.35.36.42.

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A common technique for finding accurate solutions to quadratic equations is to employ genetic algorithms. The authors propose using a genetic algorithm to find the complex roots of a quadratic problem. The technique begins by generating a collection of viable solutions, then proceeds to assess the suitability of each solution, choose parents for the next generation, and apply crossover and mutation to the offspring. For a predetermined number of generations, the process is repeated. Comparing the evolutionary algorithm's output to the quadratic formula proves its validity and uniqueness. Furth

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7

Rizky Fatih Syahputra and Yahfizham Yahfizham. "Menganalisis Konsep Dasar Algoritma Genetika." Bhinneka: Jurnal Bintang Pendidikan dan Bahasa 2, no. 1 (2023): 120–32. http://dx.doi.org/10.59024/bhinneka.v2i1.643.

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Genetic algorithms are computer techniques inspired by the theory of evolution and genetics. Individual definition, chromosome initialization, chromosome testing, selection (crossover) and mutation are fundamental elements of genetic algorithms. Genetic algorithms are used to solve optimization problems, such as lesson planning, community services and traffic light adjustment. By producing the best combination of chromosomes, the genetic algorithm can achieve ideal results. The genetic algorithm produces appropriate planning data to avoid delays. This research uses the methods of data collecti

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8

Carnahan, J., and R. Sinha. "Nature's algorithms [genetic algorithms]." IEEE Potentials 20, no. 2 (2001): 21–24. http://dx.doi.org/10.1109/45.954644.

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9

M., Nishidhar Babu, Kiran Y., and Ramesh |. V. Rajendra A. "Tackling Real Coded Genetic Algorithms." International Journal of Trend in Scientific Research and Development 2, no. 1 (2017): 217–23. https://doi.org/10.31142/ijtsrd5905.

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Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with t

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10

Kim, Yong-Hyuk, Zong Woo Geem, and Yourim Yoon. "Population-Based Redundancy Control in Genetic Algorithms: Enhancing Max-Cut Optimization." Mathematics 13, no. 9 (2025): 1409. https://doi.org/10.3390/math13091409.

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The max-cut problem is a well-known topic in combinatorial optimization, with a wide range of practical applications. Given its NP-hard nature, heuristic approaches—such as genetic algorithms, tabu search, and harmony search—have been extensively employed. Recent research has demonstrated that harmony search can outperform genetic algorithms by effectively avoiding redundant searches, a strategy similar to tabu search. In this study, we propose a modified genetic algorithm that integrates tabu search to enhance solution quality. By preventing repeated exploration of previously visited solution

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11

EZZIANE, ZOHEIR. "Solving the 0/1 knapsack problem using an adaptive genetic algorithm." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 1 (2002): 23–30. http://dx.doi.org/10.1017/s0890060401020030.

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Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures

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12

Burke, Donald S., Kenneth A. De Jong, John J. Grefenstette, Connie Loggia Ramsey, and Annie S. Wu. "Putting More Genetics into Genetic Algorithms." Evolutionary Computation 6, no. 4 (1998): 387–410. http://dx.doi.org/10.1162/evco.1998.6.4.387.

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The majority of current genetic algorithms (GAs), while inspired by natural evolutionary systems, are seldom viewed as biologically plausible models. This is not a criticism of GAs, but rather a reflection of choices made regarding the level of abstraction at which biological mechanisms are modeled, and a reflection of the more engineering-oriented goals of the evolutionary computation community. Understanding better and reducing this gap between GAs and genetics has been a central issue in an interdisciplinary project whose goal is to build GA-based computational models of viral evolution. Th

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13

Megson, G. M., and I. M. Bland. "Generic systolic array for genetic algorithms." IEE Proceedings - Computers and Digital Techniques 144, no. 2 (1997): 107. http://dx.doi.org/10.1049/ip-cdt:19971126.

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14

Frenzel, J. F. "Genetic algorithms." IEEE Potentials 12, no. 3 (1993): 21–24. http://dx.doi.org/10.1109/45.282292.

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15

Fulkerson, William F. "Genetic Algorithms." Journal of the American Statistical Association 97, no. 457 (2002): 366. http://dx.doi.org/10.1198/jasa.2002.s468.

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16

Forrest, Stephanie. "Genetic algorithms." ACM Computing Surveys 28, no. 1 (1996): 77–80. http://dx.doi.org/10.1145/234313.234350.

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17

Holland, John H. "Genetic Algorithms." Scientific American 267, no. 1 (1992): 66–72. http://dx.doi.org/10.1038/scientificamerican0792-66.

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18

Coley, D. A. "Genetic algorithms." Contemporary Physics 37, no. 2 (1996): 145–54. http://dx.doi.org/10.1080/00107519608230341.

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19

Grupe, Fritz H., and Simon Jooste. "Genetic algorithms." Information Management & Computer Security 12, no. 3 (2004): 288–97. http://dx.doi.org/10.1108/09685220410542624.

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20

Skomorokhov, Alexander O. "Genetic algorithms." ACM SIGAPL APL Quote Quad 26, no. 4 (1996): 97–106. http://dx.doi.org/10.1145/253417.253399.

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21

Holland, John. "Genetic algorithms." Scholarpedia 7, no. 12 (2012): 1482. http://dx.doi.org/10.4249/scholarpedia.1482.

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22

Alfonseca, Manuel. "Genetic algorithms." ACM SIGAPL APL Quote Quad 21, no. 4 (1991): 1–6. http://dx.doi.org/10.1145/114055.114056.

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23

Turčaník, Michal, and Martin Javurek. "Cryptographic Key Generation by Genetic Algorithms." Information & Security: An International Journal 43, no. 1 (2019): 54–61. http://dx.doi.org/10.11610/isij.4305.

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24

Agapie, Alexandru. "Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms." Evolutionary Computation 9, no. 2 (2001): 127–46. http://dx.doi.org/10.1162/106365601750190370.

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Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5

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25

Wei, Wei, Liang Liu, Zhong Qin Hu, and Yu Jing Zhou. "Rigid Medical Image Registration Based on Genetic Algorithms and Mutual Information." Applied Mechanics and Materials 665 (October 2014): 712–17. http://dx.doi.org/10.4028/www.scientific.net/amm.665.712.

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With the variety of medical imaging equipment’s application in the medical process,medical image registration becomes particularly important in the field of medical image processing,which has important clinical diagnostic and therapeutic value. This article describes the matrix conversion method of the rigid registration model, the basic concepts and principles of the mutual information algorithm ,the basic idea of genetic algorithms and algorithm’s flow , and the application of the improved genetic algorithms in practice. The rigid registration of two CT brain bones images uses mutual informa

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26

Kanwal, Maxinder S., Avinash S. Ramesh, and Lauren A. Huang. "A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms." F1000Research 2 (November 19, 2013): 139. http://dx.doi.org/10.12688/f1000research.2-139.v2.

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Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderiva

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27

D., KARDASH, and KOLLAROV O. "Solving optimization problems in energy with genetic algorithm." Journal of Electrical and power engineering 28, no. 1 (2023): 37–41. http://dx.doi.org/10.31474/2074-2630-2023-1-37-41.

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The article discusses the application of genetic algorithms in the field of energy optimization. Linear programming is commonly used for optimization problems in energy systems. Linear programming is a mathematical optimization method that seeks the optimal solution under constraints, where all constraints and the objective function are linear functions. In the realm of artificial intelligence,genetic algorithms are employed for optimization tasks. genetic algorithms mimic natural evolution processes, including selection, crossover, mutation, and adaptation, to solve optimization and search pr

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28

Chapman, C. D., K. Saitou, and M. J. Jakiela. "Genetic Algorithms as an Approach to Configuration and Topology Design." Journal of Mechanical Design 116, no. 4 (1994): 1005–12. http://dx.doi.org/10.1115/1.2919480.

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The genetic algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology design. An overview of the genetic algorithm will first describe the genetics-based representations and operators used in a typical genetic algorithm search. Then, a review of previous research in structural optimization is provided. A discretized design representation, and methods for mapping genetic algorithm “chromosomes” into this representation, is then detailed. Several examples of genetic algorithm-based structural topology optimization are p

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29

Chernov, Ivan E., and Andrey V. Kurov. "APPLICATION OF GENETIC ALGORITHMS IN CRYPTOGRAPHY." RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, no. 1 (2022): 63–82. http://dx.doi.org/10.28995/2686-679x-2022-1-63-82.

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Currently in the development of computer technologies that ensure information security and information protection, cryptographic methods of protection are widely used. The main tasks in cryptography are the development of new encryption features, difficult to break and repetitive ciphers. To solve that problem, falling into the class of NP-complete ones, algorithms based on natural principles have been used in recent years. These include genetic algorithms (GA), evolutionary methods, swarm intelligence algorithms. In models and algorithms of evolutionary computations, the construction of basic

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30

Patel, Roshni V., and Jignesh S. Patel. "Optimization of Linear Equations using Genetic Algorithms." Indian Journal of Applied Research 2, no. 3 (2011): 56–58. http://dx.doi.org/10.15373/2249555x/dec2012/19.

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31

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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32

Shi, Jiahe. "Fourier Filtering Denoising Based on Genetic Algorithms." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (2017): 1142–62. http://dx.doi.org/10.31142/ijtsrd2420.

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CHIRIAC, Liubomir, Natalia LUPAŞCO, and Maria PAVEL. "Development of genetic algorithms from inter/transdisciplinary perspectives." Acta et commentationes: Științe ale Educației 33, no. 3 (2023): 31–42. http://dx.doi.org/10.36120/2587-3636.v33i3.31-42.

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The theoretical-practical foundations of Genetic Algorithms, which are built on the principle of "survival of the fittest", enunciated by Charles Darwin, are dealt with in this paper. The paper describes the basic characteristics of the genetic algorithm, highlighting its advantages and disadvantages. Genetic algorithm problems are examined. The Genetic Algorithm is examined from the perspective of examining problems in which finding the optimal solution is not simple or at least inefficient due to the characteristics of the probabilistic search. The steps are shown in which Genetic Algorithms

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34

Aivaliotis-Apostolopoulos, Panagiotis, and Dimitrios Loukidis. "Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization." PLOS ONE 17, no. 9 (2022): e0275094. http://dx.doi.org/10.1371/journal.pone.0275094.

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Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid

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35

Ran, Limin, Shengnan Ran, and Chunmei Meng. "Green city logistics path planning and design based on genetic algorithm." PeerJ Computer Science 9 (May 5, 2023): e1347. http://dx.doi.org/10.7717/peerj-cs.1347.

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Effective logistics distribution paths are crucial in enhancing the fundamental competitiveness of an enterprise. This research introduces the genetic algorithm for logistics routing to address pertinent research issues, such as suboptimal scheduling of time-sensitive orders and reverse distribution of goods. It proposes an enhanced scheme integrating the Metropolis criterion. To address the limited local search ability of the genetic algorithm, this study combines the simulated annealing algorithm’s powerful local optimization capability with the genetic algorithm, thereby developing a geneti

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36

Mitchell, Melanie, and Stephanie Forrest. "Genetic Algorithms and Artificial Life." Artificial Life 1, no. 3 (1994): 267–89. http://dx.doi.org/10.1162/artl.1994.1.3.267.

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Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, giving illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

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37

Liang, W. Y., and Peter O'Grady. "Genetic algorithms for design for assembly: The remote constrained genetic algorithm." Computers & Industrial Engineering 33, no. 3-4 (1997): 593–96. http://dx.doi.org/10.1016/s0360-8352(97)00200-3.

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38

LEE, In-Ho. "Modern Genetic Algorithms." Physics and High Technology 27, no. 1/2 (2018): 8–11. http://dx.doi.org/10.3938/phit.27.002.

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39

Anderson-Cook, Christine M. "Practical Genetic Algorithms." Journal of the American Statistical Association 100, no. 471 (2005): 1099. http://dx.doi.org/10.1198/jasa.2005.s45.

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Stanoyevitch, Alexander. "Homogeneous genetic algorithms." International Journal of Computer Mathematics 87, no. 3 (2010): 476–90. http://dx.doi.org/10.1080/00207160801968770.

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41

Chiou, Yu-Chiun, and Lawrence W. Lan. "Genetic clustering algorithms." European Journal of Operational Research 135, no. 2 (2001): 413–27. http://dx.doi.org/10.1016/s0377-2217(00)00320-9.

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Harada, Tomohiro, and Enrique Alba. "Parallel Genetic Algorithms." ACM Computing Surveys 53, no. 4 (2020): 1–39. http://dx.doi.org/10.1145/3400031.

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43

Baum, Eric B., Dan Boneh, and Charles Garrett. "Where Genetic Algorithms Excel." Evolutionary Computation 9, no. 1 (2001): 93–124. http://dx.doi.org/10.1162/10636560151075130.

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We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a genetic-type algorithm bests all known competitors. We generalize ASP to k-ASP to study whether GAs will achieve “implicit parallelism” in a problem with many more schemata. GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel

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44

Li, He, and Naiyu Shi. "Application of Genetic Optimization Algorithm in Financial Portfolio Problem." Computational Intelligence and Neuroscience 2022 (July 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/5246309.

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In order to address the application of genetic optimization algorithms to financial investment portfolio issues, the optimal allocation rate must be high and the risk is low. This paper uses quadratic programming algorithms and genetic algorithms as well as quadratic programming algorithms, Matlab planning solutions for genetic algorithms, and genetic algorithm toolboxes to solve Markowitz’s mean variance model. The mathematical model for introducing sparse portfolio strategies uses the decomposition method of penalty functions as an algorithm for solving nonconvex sparse optimization strategi

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45

Hulianytskyi, Leonid, and Sergii Chornozhuk. "Genetic Algorithm with New Stochastic Greedy Crossover Operator for Protein Structure Folding Problem." Cybernetics and Computer Technologies, no. 2 (July 24, 2020): 19–29. http://dx.doi.org/10.34229/2707-451x.20.2.3.

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Introduction. The spatial protein structure folding is an important and actual problem in biology. Considering the mathematical model of the task, we can conclude that it comes down to the combinatorial optimization problem. Therefore, genetic and mimetic algorithms can be used to find a solution. The article proposes a genetic algorithm with a new greedy stochastic crossover operator, which differs from classical approaches with paying attention to qualities of possible ancestors. The purpose of the article is to describe a genetic algorithm with a new greedy stochastic crossover operator, re

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46

Mishra, Bhabani Shankar Prasad, Subhashree Mishra, and Sudhansu Sekhar Singh. "Parallel Multi-Criterion Genetic Algorithms." International Journal of Applied Evolutionary Computation 7, no. 1 (2016): 50–62. http://dx.doi.org/10.4018/ijaec.2016010104.

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The objective of this paper is to study the existing and current research on parallel multi-objective genetic algorithms (PMOGAs) through an intensive experiment. Many early efforts on parallelizing multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution of them with various examples. Further, the authors tried to identify some of the issues that have not yet been studied systematically under the umbrella of parallel multi-objective genetic algorithms. Finally, some of the potential application of parallel multi objective genetic

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Ankita, Ankita, and Rakesh Kumar. "Hybrid Simulated Annealing: An Efficient Optimization Technique." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7s (2023): 45–53. http://dx.doi.org/10.17762/ijritcc.v11i7s.6975.

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Genetic Algorithm falls under the category of evolutionary algorithm that follows the principles of natural selection and genetics, where the best adapted individuals in a population are more likely to survive and reproduce, passing on their advantageous traits to their offsprings. Crossover is a crucial operator in genetic algorithms as it allows the genetic material of two or more individuals in the population to combine and create new individuals. Optimizing it can potentially lead to better solutions and faster convergence of the genetic algorithm. The proposed crossover operator gradually

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48

Nurserik, D., F. R. Gusmanova, G. А. Abdulkarimova, and K. S. Dalbekova. "OVERVIEW OF HEURISTIC AND METAHEURISTIC ALGORITHMS." BULLETIN Series of Physics & Mathematical Sciences 71, no. 3 (2020): 242–47. http://dx.doi.org/10.51889/2020-3.1728-7901.37.

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The article discusses the use of heuristic algorithms for optimization problems. The algorithms for stochastic optimization are described, which constitute the main properties of the metaheuristic and its classes. Evolutionary algorithms are described in general terms. In particular, the main steps and properties of genetic algorithms are presented. The main goal of this article is to solve the vehicle routing problem using a metaheuristic algorithm. The vehicle routing problem is a complex combinatorial NP-complete optimization problem. It is shown that the metaheuristic approach to solving t

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Dharani Pragada, Venkata Aditya, Akanistha Banerjee, and Srinivasan Venkataraman. "OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM." Proceedings of the Design Society 1 (July 27, 2021): 2339–48. http://dx.doi.org/10.1017/pds.2021.495.

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AbstractAn efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design

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50

Sherstnev, Pavel A., and Evgeniy S. Semenkin. "Self-configuring genetic programming algorithms with Success History-based Adaptation." Siberian Aerospace Journal 26, no. 1 (2025): 60–70. https://doi.org/10.31772/2712-8970-2025-26-1-60-70.

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In this work, a novel method for self-tuning genetic programming (GP) algorithms is presented, based on the ideas of the Success History based Parameter Adaptation (SHA) method, originally developed for the Differential Evolution (DE) algorithm. The main idea of the method is to perform a dynamic analysis of the history of successful solutions to adapt the algorithm's parameters during the search process. To implement this concept, the operation scheme of classical GP was modified to mimic the DE scheme, allowing the integration of the success history mechanism into GP. The resulting algorithm

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