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Journal articles on the topic 'Genetic algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Kallab, Chadi, Samir Haddad, and Jinane Sayah. "Flexible Traceable Generic Genetic Algorithm." Open Journal of Applied Sciences 12, no. 06 (2022): 877–91. http://dx.doi.org/10.4236/ojapps.2022.126060.

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2

Neville, Melvin, and Anaika Sibley. "Developing a generic genetic algorithm." ACM SIGAda Ada Letters XXIII, no. 1 (2003): 45–52. http://dx.doi.org/10.1145/1066404.589462.

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3

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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Chouh, M., and K. Boukhetala. "Semi-nonnegative Matrix Factorization Algorithm Based on Genetic Algorithm Initialization." International Journal of Machine Learning and Computing 6, no. 4 (2016): 231·—234. http://dx.doi.org/10.18178/ijmlc.2016.6.4.603.

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Kandeeban, Selvakani S., and R. S. Rajesh. "Desegregated ID Execution Using Genetic Algorithm." International Journal of Engineering and Technology 1, no. 1 (2009): 45–49. http://dx.doi.org/10.7763/ijet.2009.v1.8.

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6

OKABE, Hidehiko. "Genetic Algorithm." Journal of Japan Society for Fuzzy Theory and Systems 3, no. 4 (1991): 626–38. http://dx.doi.org/10.3156/jfuzzy.3.4_2.

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7

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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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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9

Anfyorov, M. A. "Genetic clustering algorithm." Russian Technological Journal 7, no. 6 (2020): 134–50. http://dx.doi.org/10.32362/2500-316x-2019-7-6-134-150.

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The genetic algorithm of clustering of analysis objects in different data domains has been offered within the hybrid concept of intelligent information technologies development aimed to support decision-making. The algorithm makes it possible to account for different preferences of the analyst in clustering reflected in a calculation formula of fitness function. The place of this algorithm among those used for cluster analysis has been shown. The algorithm is simple in its program implementation, which increases its usage reliability. The used technology of evolutionary modeling is rather expa

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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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11

Dan Liu, Dan Liu, Shu-Wen Yao Dan Liu, Hai-Long Zhao Shu-Wen Yao, et al. "Research on Mutual Information Feature Selection Algorithm Based on Genetic Algorithm." 電腦學刊 33, no. 6 (2022): 131–41. http://dx.doi.org/10.53106/199115992022123306011.

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<p>Feature selection is an important part of data preprocessing. Feature selection algorithms that use mutual information as evaluation can effectively handle different types of data, so it has been widely used. However, the potential relationship between relevance and redundancy in the evaluation criteria is often ignored, so that effective feature subsets cannot be selected. Optimize the evaluation criteria of the mutual information feature selection algorithm and propose a mutual information feature selection algorithm based on dynamic penalty factors (Dynamic Penalty Factor Mutual In

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12

Lie, Luo. "Heuristic Artificial Intelligent Algorithm for Genetic Algorithm." Key Engineering Materials 439-440 (June 2010): 516–21. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.516.

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A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

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13

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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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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15

Huang, Xiabao, Zailin Guan, and Lixi Yang. "An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem." Advances in Mechanical Engineering 10, no. 9 (2018): 168781401880144. http://dx.doi.org/10.1177/1687814018801442.

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Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into gen

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16

Riwanto, Yudha, Muhammad Taufiq Nuruzzaman, Shofwatul Uyun, and Bambang Sugiantoro. "Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method." IJID (International Journal on Informatics for Development) 11, no. 2 (2023): 222–31. http://dx.doi.org/10.14421/ijid.2022.3743.

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High accuracy and speed in data search, which are aims at finding the best solution to a problem, are essential. This study examines the brute force method, genetic algorithm, and two proposed algorithms which are the development of the brute force algorithm and genetic algorithm, namely Multiple Crossover Genetic, and Genetics with increments values. Brute force is a method with a direct approach to solving a problem based on the formulation of the problem and the definition of the concepts involved. A genetic algorithm is a search algorithm that uses genetic evolution that occurs in living t

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17

J Fernandes, R., and Sushmita Yadahalli. "Optimization of Shell Structure Using Genetic Algorithm." International Journal of Science and Research (IJSR) 11, no. 8 (2022): 171–72. http://dx.doi.org/10.21275/sr22719181219.

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18

DR, Shilpa, and Uma BV. "Crosstalk Minimization in SOC using Genetic Algorithm." International Journal of Scientific Engineering and Research 5, no. 4 (2017): 144–47. https://doi.org/10.70729/ijser151359.

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19

Mathur, Y. P., and S. J. Nikam. "Optimal Reservoir Operation Policies Using Genetic Algorithm." International Journal of Engineering and Technology 1, no. 2 (2009): 184–87. http://dx.doi.org/10.7763/ijet.2009.v1.34.

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20

U Kharat, G., R. S Bansode, and H. A Chavan. "Genetic Algorithm for Node Localization in WSN." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 2162–64. https://doi.org/10.21275/sr25422221028.

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21

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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22

Ouiss, M., A. Ettaoufik, A. Marzak, and A. Tragha. "Genetic algorithm parenting fitness." Mathematical Modeling and Computing 10, no. 2 (2023): 566–74. http://dx.doi.org/10.23939/mmc2023.02.566.

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The evolution scheme phase, in which the genetic algorithms select individuals that will form the new population, had an important impact on these algorithms. Many approaches exist in the literature. However, these approaches consider only the value of the fitness function to differenciate best solutions from the worst ones. This article introduces the parenting fitness, a novel parameter, that defines the capacity of an individual to produce fittest offsprings. Combining the standard fitness function and the parenting fitness helps the genetic algorithm to be more efficient, hence, producing

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23

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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24

Nabil, Emad, Amr Badr, and Ibrahim Farag. "An Immuno-Genetic Hybrid Algorithm." International Journal of Computers Communications & Control 4, no. 4 (2009): 374. http://dx.doi.org/10.15837/ijccc.2009.4.2454.

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The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a m

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25

Guo, Mei Ni. "Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem." Applied Mechanics and Materials 365-366 (August 2013): 194–98. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.194.

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mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optim

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26

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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27

Vandeva, Elica. "MultiObjective Genetic Modified Algorithm (MOGMA)." Cybernetics and Information Technologies 12, no. 2 (2012): 23–33. http://dx.doi.org/10.2478/cait-2012-0010.

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Abstract Multiobjective optimization based on genetic algorithms and Pareto based approaches in solving multiobjective optimization problems is discussed in the paper. A Pareto based fitness assignment is used − non-dominated ranking and movement of a population towards the Pareto front in a multiobjective optimization problem. A MultiObjective Genetic Modified Algorithm (MOGMA) is proposed, which is an improvement of the existing algorithm.

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HIRASAWA, Kotaro, Yasutaka ISHIKAWA, Jinglu HU, and Junichi MURATA. "Genetic Symbiosis Algorithm." Transactions of the Society of Instrument and Control Engineers 35, no. 9 (1999): 1198–206. http://dx.doi.org/10.9746/sicetr1965.35.1198.

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29

Gutowski, M. W. "Smooth genetic algorithm." Journal of Physics A: Mathematical and General 27, no. 23 (1994): 7893–904. http://dx.doi.org/10.1088/0305-4470/27/23/032.

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30

Zhao, Xinchao, and Xiao-Shan Gao. "Affinity genetic algorithm." Journal of Heuristics 13, no. 2 (2007): 133–50. http://dx.doi.org/10.1007/s10732-006-9005-z.

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31

Al Rivan, Muhammad Ezar, and Bhagaskara Bhagaskara. "Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan." Jurnal Sisfokom (Sistem Informasi dan Komputer) 9, no. 3 (2020): 350. http://dx.doi.org/10.32736/sisfokom.v9i3.879.

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The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using a Genetic Algorithm (GA). GA has been proven to be able to provide a schedule that can meet limitations in scheduling. Besides, it also found a new concept of thought from GA, namely the Fluid Genetic Algorithm (FGA). The most visible difference between FGA and GA is that there is no mutation process in each iteration. FGA has a new stage, nam

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Berisha, Artan, Eliot Bytyçi, and Ardeshir Tershnjaku. "Parallel Genetic Algorithms for University Scheduling Problem." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 1096. http://dx.doi.org/10.11591/ijece.v7i2.pp1096-1102.

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University scheduling timetabling problem, falls into NP hard problems. Re-searchers have tried with many techniques to find the most suitable and fastest way for solving the problem. With the emergence of multi-core systems, the parallel implementation was considered for finding the solution. Our approaches attempt to combine several techniques in two algorithms: coarse grained algorithm and multi thread tournament algorithm. The results obtained from two algorithms are compared, using an algorithm evaluation function. Considering execution time, the coarse grained algorithm performed twice b

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Artan, Berisha, Bytyci Eliot, and Tershnjaku Ardeshir. "Parallel Genetic Algorithms for University Scheduling Problem." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 2 (2017): 1096–102. https://doi.org/10.11591/ijece.v7i2.pp1096-1102.

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University scheduling timetabling problem, falls into NP hard problems. Re-searchers have tried with many techniques to find the most suitable and fastest way for solving the problem. With the emergence of multi-core systems, the parallel implementation was considered for finding the solution. Our approaches attempt to combine several techniques in two algorithms: coarse grained algorithm and multi thread tournament algorithm. The results obtained from two algorithms are compared, using an algorithm evaluation function. Considering execution time, the coarse grained algorithm performed twice b

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34

Trabia, Mohamed B. "A Hybrid Fuzzy Simplex Genetic Algorithm." Journal of Mechanical Design 126, no. 6 (2004): 969–74. http://dx.doi.org/10.1115/1.1803852.

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This paper presents a novel hybrid genetic algorithm that has the ability of the genetic algorithms to avoid being trapped at local minimum while accelerating the speed of local search by using the fuzzy simplex algorithm. The new algorithm is labeled the hybrid fuzzy simplex genetic algorithm (HFSGA). Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The HFSGA generally results in a faster convergence toward extremum.

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Sivalakshmi, Bolem, and N. Naga Malleswara Rao. "Microarray Image Analysis Using Genetic Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (2016): 561. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp561-567.

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<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. This paper presents microarray image analysis using Genetic Algorithm. A new algorithm for microarray image contrast enhancement is presented using Genetic Algorithm. Contrast enhancement is crucial step in e

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36

Tyagi, Khushali, Deepak Kumar, and Richa Gupta. "Application of Genetic Algorithms for Medical Diagnosis of Diabetes Mellitus." International Journal of Experimental Research and Review 37 (March 30, 2024): 1–10. http://dx.doi.org/10.52756/ijerr.2024.v37spl.001.

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The system of glucose-insulin control and associated problems in diabetes mellitus were studied by mathematical modeling. It is a helpful theoretical tool for understanding the basic concepts of numerous distinct medical and biological functions. It delves into the various risk factors contributing to the onset of diabetes, such as sedentary lifestyle, obesity, family history, viruses, and increasing age. The study emphasizes the importance of mathematical models in understanding the dynamic characteristics of biological systems. The study emphasizes the increasing prevalence of diabetes, espe

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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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Shinkarenko, V. I., and O. V. Makarov. "Genetic algorithm for structural adaptation of sorting algorithms." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 11–18. https://doi.org/10.15407/pp2024.02-03.011.

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Constructivism was applied to form the sorting algorithm code. The meta-algorithm of program code generation is presented. Parts of existing sorting algorithms and auxiliary utilities are used for generation. A genetic algorithm was used to select the algorithm with the maximum time efficiency under the given conditions of use. The use of a standard genetic algorithm faces a problem associated with a different number of elementary sorting operations, which leads to the use of chromosomes of diff erent lengths. To solve the problem, a representation of the chromosome in the form of a binary tre

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Ding, Lei, Yong Jun Luo, Yang Yang Wang, Zheng Li, and Bing Yin Yao. "Improved Method of Hybrid Genetic Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 4014–17. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4014.

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On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

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LIN, Feng. "Improved genetic operator for genetic algorithm." Journal of Zhejiang University SCIENCE 3, no. 4 (2002): 431. http://dx.doi.org/10.1631/jzus.2002.0431.

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Feng, Lin, and Yang Qi-wen. "Improved genetic operator for genetic algorithm." Journal of Zhejiang University-SCIENCE A 3, no. 4 (2002): 431–34. http://dx.doi.org/10.1631/bf02839485.

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42

Zhang, Qiang. "An optimized solution to the course scheduling problem in universities under an improved genetic algorithm." Journal of Intelligent Systems 31, no. 1 (2022): 1065–73. http://dx.doi.org/10.1515/jisys-2022-0114.

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Abstract The increase in the size of universities has greatly increased the number of teachers, students, and courses and has also increased the difficulty of scheduling courses. This study used coevolution to improve the genetic algorithm and applied it to solve the course scheduling problem in universities. Finally, simulation experiments were conducted on the traditional and improved genetic algorithms in MATLAB software. The results showed that the improved genetic algorithm converged faster and produced better solutions than the traditional genetic algorithm under the same crossover and m

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Wang, Hong Tao. "The Study on Neural Network Intelligent Method Based on Genetic Algorithm." Advanced Materials Research 271-273 (July 2011): 546–51. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.546.

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The paper gives the hybrid computational intelligence learning algorithm with global convergence, which is combined by BP algorithm and genetic algorithm. This algorithm connects the strengths of the BP algorithm and genetic algorithms. It not only has faster convergence, but also has a good global convergence property. The computer simulation results show that the hybrid algorithm is significantly better than the genetic algorithm and BP algorithm.

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Adyan, Nur Alfiyatin, Firdaus Mahmudy Wayan, and Priyo Anggodo Yusuf. "K-Means Clustering and Genetic Algorithm to Solve Vehicle Routing Problem with Time Windows Problem." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 2 (2018): 462–68. https://doi.org/10.11591/ijeecs.v11.i2.pp462-468.

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Distribution is an important aspect of industrial activity to serve customers on time with minimal operational cost. Therefore, it is necessary to design a quick and accurate distribution route. One of them can be design travel distribution route using k-means method and genetic algorithms. This research will combine k-means method and genetic algorithm to solve vehicle routing problem with time windows (VRPTW). K-means can do clustering properly and genetic algorithms can optimize the route. The proposed genetic algorithm employs initialize chromosome from the result of k-means and using repl

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45

Wirayanti, Ni Komang Ayu, and Haris Sriwindono. "Implementation of Hybrid Genetic Algorithm for Solving the Teacher Placement Problem." Social Science and Humanities Journal 9, no. 01 (2025): 6341–47. https://doi.org/10.18535/sshj.v9i01.1460.

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The teacher placement problem is a combinatorial problem that would take a very long time to solve in a deterministic way. In this study, the problem will be solved using a hybrid genetic algorithm, which combines genetic algorithms with local search methods. The genetic algorithm operators used include roulette wheel selection, two point crossover, and scramble mutation. While the local search used is reverse, insert, and swap local search. The results showed that from the three experiments using hybrid genetic algorithms, it was found that hybrid genetic algorithms were more effective than o

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Skorpil, Vladislav, and Vaclav Oujezsky. "Parallel Genetic Algorithms’ Implementation Using a Scalable Concurrent Operation in Python." Sensors 22, no. 6 (2022): 2389. http://dx.doi.org/10.3390/s22062389.

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This paper presents an implementation of the parallelization of genetic algorithms. Three models of parallelized genetic algorithms are presented, namely the Master–Slave genetic algorithm, the Coarse-Grained genetic algorithm, and the Fine-Grained genetic algorithm. Furthermore, these models are compared with the basic serial genetic algorithm model. Four modules, Multiprocessing, Celery, PyCSP, and Scalable Concurrent Operation in Python, were investigated among the many parallelization options in Python. The Scalable Concurrent Operation in Python was selected as the most favorable option,

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Ševela, Marcel. "Applicability of genetic algorithms to parameter estimation of economic models." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 52, no. 3 (2004): 79–86. http://dx.doi.org/10.11118/actaun200452030079.

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The paper concentrates on capability of genetic algorithms for parameter estimation of non-linear economic models. In the paper we test the ability of genetic algorithms to estimate of parameters of demand function for durable goods and simultaneously search for parameters of genetic algorithm that lead to maximum effectiveness of the computation algorithm. The genetic algorithms connect deterministic iterative computation methods with stochastic methods. In the genteic aůgorithm approach each possible solution is represented by one individual, those life and lifes of all generations of indivi

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Kumar, V. Sivaram, M. R. Thansekhar, and R. Saravanan. "A New Multi Objective Genetic Algorithm: Fitness Aggregated Genetic Algorithm (FAGA) for Vehicle Routing Problem." Advanced Materials Research 984-985 (July 2014): 1261–68. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.1261.

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This paper presents multi objective vehicle routing problem in which the total distance travelled by the vehicles and total number of vehicles used are minimized. In general, fitness assignment procedure, as one of the important operators, influences the effectiveness of multi objective genetic algorithms. In this paper genetic algorithm with different fitness assignment approach and specialized crossover called Fitness Aggregated Genetic Algorithm (FAGA) is introduced for solving the problem. The suggested algorithm is investigated on large number of popular benchmarks for vehicle routing pro

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Quang Hung, Nguyen, Tu Ngoc Anh Nguyen, and Nam Thoai. "Parallel approaches of genetic algorithm in the MIC architecture of the Intel Xeon Phi." Science & Technology Development Journal - Engineering and Technology 2, no. 4 (2020): 277–87. http://dx.doi.org/10.32508/stdjet.v2i4.612.

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Today, genetic algorithms are widely used in many fields such as bioinformatics, computer science, artificial intelligence, finance ... Genetic algorithms are applied to create high quality solutions for complex optimization problems in the above industries. There have been many studies based on the proposed new hardware architecture that aims to speed up the execution of genetic algorithms as quickly as possible. Some studies suggest parallel genetic algorithms on systems with multicore CPUs and / or graphics processing units (GPUs). However, very few solutions propose a genetic algorithm tha

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Comlan, Maurice, and Corentin Allohoumbo. "Constraint satisfaction algorithms: edition of timetables in the license-master-doctorate system." Computer Science and Information Technologies 4, no. 3 (2023): 217–26. http://dx.doi.org/10.11591/csit.v4i3.pp217-226.

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In this paper, we studied some algorithms for solving constraint satisfaction problem (CSP) and then applied them to solve the problem of generating schedules in a university setting. In other words, we studied the genetic algorithm, the simulated annealing, the hill climbing, a hybridization of the genetic algorithm and the simulated annealing as well as a hybridization of the genetic algorithm and the hill climbing. These algorithms have been tested on the problem of scheduling in a university environment. The hybrid uses hill climbing or simulated annealing to improve each individual in the

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