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Journal articles on the topic 'Genetic algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 March 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 (March 2003): 45–52. http://dx.doi.org/10.1145/1066404.589462.

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3

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 (August 2016): 231·—234. http://dx.doi.org/10.18178/ijmlc.2016.6.4.603.

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4

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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5

Rizky Fatih Syahputra and Yahfizham Yahfizham. "Menganalisis Konsep Dasar Algoritma Genetika." Bhinneka: Jurnal Bintang Pendidikan dan Bahasa 2, no. 1 (December 23, 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 collection, individual definition and chromosome initialization. The result of this research is a service application designed to be able to plan efficiently through development using a genetic algorithm. Optimal planning occurs when processing planning data produces solutions that are efficient in terms of time, energy, and resources, and avoids conflicting schedules in the same place..

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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

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 (September 23, 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 algorithm that nests particle swarm optimization operations in the genetic algorithm, providing the general population with the exploitation prowess of the genetic algorithm and a sub-population with the high exploitation capabilities of particle swarm optimization. The effectiveness of the proposed algorithm is demonstrated through solutions of several continuous optimization problems, as well as discrete (traveling salesman) problems. It is found that the new hybrid algorithm provides a better balance between exploration and exploitation compared to both parent algorithms, as well as existing hybrid algorithms, achieving consistently accurate results with relatively small computational cost.

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Anfyorov, M. A. "Genetic clustering algorithm." Russian Technological Journal 7, no. 6 (January 10, 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 expanded in the mentioned algorithm. Firstly, the decimal chromosomes coding is used instead of the traditional binary coding. This has resulted from the fact that the chromosome genes condition is multiple and not binary. Moreover, this is due to the absence of the genetic operator of inversion in this algorithm. Secondly, a new genetic operator used for filtering has been implemented. This operator eliminates chromosomes that do not meet the required clusters quantity condition in a task. Such chromosomes can appear in the stochastic process of their evolution. The presented algorithm has been studied in a series of simulation experiments. As a result, it has been found that stabilization of splitting into clusters is reached when the number of completed generations of evolution is 200 and more, and the population size is rather small: from 150 chromosomes (in this case no considerable amount of random-access store is required). The calculations carried out on real data showed for this algorithm the high quality of clustering and the acceptable computing speed of the same order with the computing speed of SOM and “k-means” algorithms.

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Dan Liu, Dan Liu, Shu-Wen Yao Dan Liu, Hai-Long Zhao Shu-Wen Yao, Xin Sui Hai-Long Zhao, Yong-Qi Guo Xin Sui, Mei-Ling Zheng Yong-Qi Guo, and Li Li Mei-Ling Zheng. "Research on Mutual Information Feature Selection Algorithm Based on Genetic Algorithm." 電腦學刊 33, no. 6 (December 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 Information Feature Selection Algorithm, DPMFS). The penalty factor is dynamically calculated with different selected features, so as to achieve a relative balance between relevance and redundancy, and effectively play the synergy between relevance and redundancy, and select a suitable feature subset. Experimental results verify that the DPMFS algorithm can effectively improve the classification accuracy of the feature selection algorithm. Compared with the traditional chi-square, MIM and MIFS feature selection algorithms, the average classification accuracy of the random forest classifier for the six standard datasets is increased by 3.73%, 3.51% and 2.44%, respectively.</p> <p> </p>

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10

EZZIANE, ZOHEIR. "Solving the 0/1 knapsack problem using an adaptive genetic algorithm." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 1 (January 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 based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

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11

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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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 pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

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13

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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14

J Fernandes, R., and Sushmita Yadahalli. "Optimization of Shell Structure Using Genetic Algorithm." International Journal of Science and Research (IJSR) 11, no. 8 (August 5, 2022): 171–72. http://dx.doi.org/10.21275/sr22719181219.

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DR, Shilpa, and Uma BV. "Crosstalk Minimization in SOC using Genetic Algorithm." International Journal of Scientific Engineering and Research 5, no. 4 (April 27, 2017): 144–47. https://doi.org/10.70729/ijser151359.

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16

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 (May 30, 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 algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.

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17

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 (July 13, 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 changes the alpha value as the search proceeds, similar to the temperature in simulated annealing. The performance of the proposed crossover operator is compared with the simple arithmetic crossover operator. The experiments are conducted using Python and results show that the proposed crossover operator outperforms the simple arithmetic crossover operator. This paper also emphasizes the importance of optimizing genetic operators, particularly crossover operators, to improve the overall performance of genetic algorithms.

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18

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 best results.

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19

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 (December 1997): 593–96. http://dx.doi.org/10.1016/s0360-8352(97)00200-3.

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20

Nabil, Emad, Amr Badr, and Ibrahim Farag. "An Immuno-Genetic Hybrid Algorithm." International Journal of Computers Communications & Control 4, no. 4 (December 1, 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 modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.

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21

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 (January 25, 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 things as its basis. This research selected the case of determining the pin series by looking for a match between the target and the search result. To test the suitability of the method, 100-time tests were conducted for each algorithm. The results of this study indicated that brute force has the highest average generation rate of 737146.3469 and an average time of 1960.4296, and the latter algorithm gets the best score with an average generation rate of 36.78 and an average time of 0.0642.

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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 (September 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 genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multi-objective flexible job-shop scheduling problem.

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23

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 models and rules is implemented, according to which it can change (evolve). In recent years, evolutionary computing schemes have been proposed, including the genetic algorithm, genetic programming, evolutionary programming, and evolutionary strategies. The paper discusses the existing cryptography methods, basic concepts and methods of modern cryptography, the notion of a genetic algorithm, a universal hash function, as well as a hash detection method and a genetic hashing algorithm built on it. A genetic algorithm was implemented in the Golang language, modified for the current problem of finding the optimal hash functions. A detailed description of each stage of the algorithm execution is given. Also, within the framework of the research, a study of the function of the genetic algorithm itself and the genetic hashing algorithm was carried out, evaluating the convergence of the genetic algorithm depending on the input data, and evaluating the possible direction of further research.

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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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25

Gutowski, M. W. "Smooth genetic algorithm." Journal of Physics A: Mathematical and General 27, no. 23 (December 7, 1994): 7893–904. http://dx.doi.org/10.1088/0305-4470/27/23/032.

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26

Zhao, Xinchao, and Xiao-Shan Gao. "Affinity genetic algorithm." Journal of Heuristics 13, no. 2 (January 30, 2007): 133–50. http://dx.doi.org/10.1007/s10732-006-9005-z.

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27

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 (September 14, 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, namely individual born and new constants, namely global learning rate, individual learning rate, and diversity rate. This concept of thinking was tested in previous studies and found that FGA is superior to GA for the problem of finding the optimum value of a predetermined function, but this function is not included in the multi-objective problem. In this study, the testing and comparison of FGA and GA were conducted for the problem of scheduling lectures at STMIK XYZ. Based on the results obtained, FGA can produce a schedule without any hard constraint violations. FGA can be used to solve multi-objective problems. FGA has a smaller number of generations than GA. However, overall GA is superior in producing schedules without any problems.

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Vandeva, Elica. "MultiObjective Genetic Modified Algorithm (MOGMA)." Cybernetics and Information Technologies 12, no. 2 (June 1, 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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CHIRIAC, Liubomir, Natalia LUPAŞCO, and Maria PAVEL. "Development of genetic algorithms from inter/transdisciplinary perspectives." Acta et commentationes: Științe ale Educației 33, no. 3 (September 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 encode a possible solution to a specific problem in a single data structure called a "chromosome" and set the stage for applying genetic operators to these structures in order to maintain critical information.

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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 (December 1, 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 extracting edge information in image and finally this edge information is used in gridding of microarray image. Mostly segmentation of microarray image is carried out using clustering algorithms. Clustering algorithms have an advantage that they are not restricted to a particular shape and size for the spots. In this paper, segmentation using Genetic Algorithm by optimizing K-means index and Jm measure is presented. The qualitative analysis shows that the proposed method achieves better segmentation results than K-means and FCM algorithms.</p>

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Trabia, Mohamed B. "A Hybrid Fuzzy Simplex Genetic Algorithm." Journal of Mechanical Design 126, no. 6 (November 1, 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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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 Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

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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 (April 1, 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 better than the multi thread algorithm.

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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 (September 2002): 431–34. http://dx.doi.org/10.1631/bf02839485.

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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 tree is proposed. Each node has start genes, end genes, and two child nodes. To form an algorithm that is guaranteed to sort the array, all end nodes (leaves) include the final sorting gene at the end of the start genes sequence. This gene is decoded by calling the existing sorting algorithm, which is guaranteed to perform sorting. Crossover and mutation operations are performed on chromosomes in the form of a binary tree. Crossover is performed using the exchange of tree branches. Mechanisms of coding and decoding of the sorting algorithm from chromosome have been implemented. Decoding and formation of a suitable sorting algorithm is performed using linearization: formation of a textual representation using a depth-first tree traversal algorithm. The fitness function is defined as the average time of sorting randomly generated arrays for sorting (identical arrays for all chromosomes) in some stable environment, considering certain features of these arrays. It is possible to use other fitness functions related to the number of calculations, comparisons, or permutations. The developed software should be used for adaptation of sorting algorithms to stable input data streams and environments.

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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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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, especially in India, where urbanization and lifestyle changes contribute to the rising incidence. The present investigation describes the use of John Holland's evolutionary computing approach and the Genetic Algorithm (GA) in diabetes mellitus. The Genetic Algorithm is applied to address issues related to diabetes, offering a generic solution and utilizing MATLAB's Genetic Algorithm tool. The Mathematical Model provides differential equations representing glucose and insulin concentrations in the blood. The results represent testing outcomes for normal, prediabetic, and diabetic individuals, optimized with Genetic Algorithm showcased through fitness value plots. The conclusion highlights the effectiveness of Genetic Algorithm as an optimization tool in predicting optimal samples for diabetes diagnosis. The paper encourages the use of heuristic algorithms, such as Genetic Algorithms, to address complex challenges in the field of diabetes research. Future scope includes further exploration of biomathematics and Genetic Algorithm applications for enhanced understanding and management of diabetes mellitus. It is critical for people with diabetes to consistently check their blood glucose levels and follow their treatment plan.

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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 problem. It is observed from the results that the suggested new algorithm is very effective and the solutions are competitive with the best known results.

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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 strategies to solve financial portfolio problems. The merging speed of the quadratic programming algorithm is fast, and the merging speed depends on the selection of the initial value. The genetic algorithm performs very well in global searches, but local search capabilities are insufficient and the pace of integration into the next stage is slow. To solve this, using a genetic algorithm toolbox is quick and easy. The results of the experiments show that the final solution of the decomposition method of the fine function is consistent with the solution of the integrity of the genetic algorithm. 67% of the total funds will be spent on local car reserves and 33% on wine reserves. When data scales are small, quadratic programming algorithms and genetic algorithms can provide effective portfolio feedback, and the method of breaking down penalty functions to ensure the reliability and effectiveness of algorithm combinations is widely used in sparse financial portfolio issues.

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Nair, Dr Prabha Shreeraj. "Clustered Genetic Algorithm to solve Multidimensional Knapsack Problem." International Journal of Trend in Scientific Research and Development Volume-1, Issue-4 (June 30, 2017): 737–45. http://dx.doi.org/10.31142/ijtsrd2237.

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Suganthy, K. Beulah. "Identification of Disease in Leaves using Genetic Algorithm." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1264–67. http://dx.doi.org/10.31142/ijtsrd22901.

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CHA, Joo-Heon, and In-Ho LEE. "Synthesis of Mechanical Structures Using a Genetic Algorithm." Proceedings of Design & Systems Conference 2004.14 (2004): 243–46. http://dx.doi.org/10.1299/jsmedsd.2004.14.243.

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Pornpanomchai, Chomtip, Verachad Wongsawangtham, Satheanpong Jeungudomporn, and Nannaphat Chatsumpun. "Thai Handwritten Character Recognition by Genetic Algorithm (THCRGA)." International Journal of Engineering and Technology 3, no. 2 (2011): 148–53. http://dx.doi.org/10.7763/ijet.2011.v3.214.

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Lakshmi, V. Rajya. "Optimization of Thinned Dipole Arrays Using Genetic Algorithm." International Journal of Engineering and Technology 3, no. 6 (2011): 658–62. http://dx.doi.org/10.7763/ijet.2011.v3.301.

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RAJALAKSHMI.M, RAJALAKSHMI M. "Software System Re-Modularization using Interactive Genetic Algorithm." Paripex - Indian Journal Of Research 3, no. 4 (January 15, 2012): 105–7. http://dx.doi.org/10.15373/22501991/apr2014/32.

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Rybickova, Ing Alena, Ing DenisaMockova Ing. DenisaMockova, and Ing Bc AdelaKaraskova Ing.Bc. AdelaKaraskova. "Application of Genetic Algorithm to The TsP Problem." Paripex - Indian Journal Of Research 3, no. 7 (January 1, 2012): 1–3. http://dx.doi.org/10.15373/22501991/july2014/35.

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YAPICI, Mutlu, and Ömer Faruk BAY. "Genetic Algorithm Based Timetabling Program." Artificial Intelligence Studies 2, no. 2 (December 26, 2020): 20–31. http://dx.doi.org/10.30855/ais.2019.02.02.01.

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Course Timetabling Problem is concerned with assigning a number of courses and instructors to classrooms by taking the constraints into consideration. Generally, this problem is typically resolved manually; and due to the large variety of constraints, resource limitations and complicated human factors involved, it takes a lot of time and manpower. It is considered as one of the most time-consuming problems faced by universities and colleges today. In this study, we aimed to develop a genetic algorithm-based timetabling software to bring a solution to course timetabling problem, which is a real world problem. This software allows constraints to be entered easily and allows that optimal solutions are found. To find the most suitable solution for optimization, two different solution methods, a full-genetic algorithm and a partial-genetic algorithm, were tested. Test results showed that when we start the full genetic algorithms from randomly generated initial population, it takes quite some time to obtain the appropriate solution. With the partial-genetic algorithm, an optimal solution was achieved much more quickly than the full genetic algorithm.

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Zhang, Yi, Zheng Liu, Hu Zhang, and Hui Fang Li. "A Crowding Niche Cellular Genetic Algorithm." Advanced Materials Research 482-484 (February 2012): 1933–36. http://dx.doi.org/10.4028/www.scientific.net/amr.482-484.1933.

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This paper presents a crowding niche cellular genetic algorithm (referred to NCGA) aiming at solving the problems of local convergence and non-uniform population distribution in traditional genetic algorithm. The selecting operation in traditional genetic algorithm is improved by bringing in the concept of neighbors of cellular genetic algorithm, and the population distribution is greatly enhanced by introducing crowding niche mechanism, which betters the ability of global searching and helps to avoid the population local convergence. Meanwhile, the paper describes the crowding niche cellular genetic algorithm in details and compares it with simple genetic algorithm （SGA） and simple niche genetic algorithm （NGA）; the comparison results reveal that, NCGA outperforms the other two algorithms in terms of convergence rate and population diversity.

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Zhang, Qiang. "An optimized solution to the course scheduling problem in universities under an improved genetic algorithm." Journal of Intelligent Systems 31, no. 1 (January 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 mutation probability. As the mutation probability in the algorithm increased, the fitness values of both genetic algorithms gradually decreased, and the computation time increased. With the increase in crossover probability in the algorithm, the fitness value of the two genetic algorithms increased first and then decreased, and the computational time decreased first and then increased.

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