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Journal articles on the topic 'Arithmetic crossover operator'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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1

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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2

Poohoi, Ratchadakorn, Kanate Puntusavase, and Shunichi Ohmori. "A novel crossover operator for genetic algorithm: Stas crossover." Decision Science Letters 12, no. 3 (2023): 515–24. http://dx.doi.org/10.5267/j.dsl.2023.4.010.

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The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, including Single point crossover, Two points crossover, Arithmetic crossover, and Scattered crossover, and then presents the performance of this crossover operator. The area size and probability of Stas crossover can be adjusted.GA is used to find the optimal solution for this multi-product and multi-

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3

Xiao, Wei Yue, Yue Hua Cai, and You Xin Luo. "Evolutionary Cellular Automata Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization." Applied Mechanics and Materials 271-272 (December 2012): 912–16. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.912.

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The optimization design about hybrid discrete variables synthesizing integer, discrete and continuous variables is very significant but also difficult in engineering, mathematics for programming and operational research. Aimed at shortages of existing optimum methods, in this paper, Evolutionary Cellar Automata Algorithm (ECAA) is proposed to complex optimization problem with hybrid discrete variables which has a digging operator and two learning operators (dual arithmetic crossover operator and chaos-peak-jumping operator). The computing examples of mechanical optimization design show that th

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4

Chen, Xi, and Xi Cheng Wang. "The Application of Improved Genetic Algorithm in Gate Location Optimization of Plastic Injection Molding." Advanced Materials Research 694-697 (May 2013): 2721–24. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2721.

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A multi-population genetic algorithm based on species equation and Kriging operator is presented in this paper. The parameters of species equation are considered as design variables and processed by real coding, the equation is regarded as modified arithmetic crossover operator to participate in genetic operation. The Kriging operator is bought in to enhance the ability of search optimal solution and promote convergence. The improved genetic algorithm, combined with Z-MOLD simulation program, is used to search the optimal gate location. The results show that the algorithm can effectively solve

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5

Al-khafaji, Amen. "Multi-Operator Genetic Algorithm for Dynamic Optimization Problems." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 3 (2017): 139–42. https://doi.org/10.5281/zenodo.4113893.

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Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the proposed framework is tested on

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Wang, Peng Jia, Chen Guang Guo, Yong Xian Liu, and Zhong Qi Sheng. "The System of Spindle Optimization Design Based on GA." Advanced Materials Research 466-467 (February 2012): 773–77. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.773.

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Aiming at the optimization design of spindle, this paper introduces deflection constraint, strength constraint, corner constraint, cutting force constraint, the limit of torsional deflection, boundary constraint of design variable, dynamic property constraint , realizes the expression of the mathematical model of the spindle optimization design. Through the introduction of the real number code rule, the selection operator is built by adopting the optimum maintaining tactics and proportional selection, the crossover operator is built by using the method of arithmetic crossover and the mutation

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Liu, Yun Lian, Wen Li, Tie Bin Wu, Yun Cheng, Tao Yun Zhou, and Gao Feng Zhu. "An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering." Advanced Materials Research 962-965 (June 2014): 2903–8. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2903.

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An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local s

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8

Ren, Chun Yu. "Study on Hybrid Genetic Algorithm for Multi-Type Vehicle Open Vehicle Routing Problem." Advanced Materials Research 204-210 (February 2011): 1287–90. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1287.

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Multi-type vehicle open vehicle routing problem is logistics optimization indispensable part. Hybrid genetic algorithm is used to optimize the solution. Firstly, use sequence of real numbers coding so as to simplify the problem; Construct the targeted initial solution to improve the feasibility; adopt some arithmetic crossover operator to enhance whole search ability of the chromosome. Secondly, Boltzmann simulated annealing mechanism for control genetic algorithm crossover and mutation operations improve the convergence speed and search efficiency. Finally, comparing to standard genetic algor

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J., Revathi, Anitha J., and Jude Hemanth D. "Training feedforward neural network using genetic algorithm to diagnose left ventricular hypertrophy." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 3 (2020): 1285–91. https://doi.org/10.12928/TELKOMNIKA.v18i3.15225.

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In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the EC

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10

Chen, Xi, and Bao Sheng Zhao. "Research on Genetic Algorithm in Mold Optimization Design." Applied Mechanics and Materials 397-400 (September 2013): 1030–33. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1030.

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Species evolution model in natural is introduced into the genetic algorithm to reflect the true laws of evolution. A multi-population genetic algorithm based on species evolution is developed. In the algorithm, the parameters of species evolution model are considered as design variables, and the equation is regarded as modified arithmetic crossover operator to participate in genetic operation. Immigration operator is used to promote convergence and enhance the ability of search optimal solution. The improved genetic algorithm is applied to mold optimization design to search the optimal gate lo

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11

Ren, Chun Yu. "Study on Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problem." Applied Mechanics and Materials 178-181 (May 2012): 1769–72. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1769.

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Capacitated vehicle routing problem is logistics optimization indispensable part. The hybrid genetic algorithm is used to optimize the solution. Firstly, use sequence of real numbers coding so as to simplify the problem; Construct the initial solution to improve the feasibility; adopt some arithmetic crossover operator to enhance whole search ability of the chromosome. Secondly, use Boltzmann simulated annealing mechanism to improve the convergence speed and search efficiency. Finally, comparing to other algorithms, the results demonstrate the effectiveness and good quality.

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12

Ren, Chun Yu. "Research on Improved Genetic Algorithm for Heterogeneous Open Vehicle Routing Problem." Applied Mechanics and Materials 55-57 (May 2011): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.859.

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This paper studies heterogeneous open vehicle routing problem. Since the standard genetic algorithm is short of convergent speed and partial searching ability as well as easily premature, improved genetic algorithm is then adopted as an optimized solution. Firstly, sequence of real numbers coding is used to simplify the problem; it may construct the initial solution pertinently in order to improve the feasibility. The individual amount control choice strategy can guard the diversity of group. The adopting some arithmetic crossover operator can enhance local search ability of the chromosome. Fi

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13

Fan, Xinnan, Linbin Pang, Pengfei Shi, Guangzhi Li, and Xuewu Zhang. "Application of Bee Evolutionary Genetic Algorithm to Maximum Likelihood Direction-of-Arrival Estimation." Mathematical Problems in Engineering 2019 (February 25, 2019): 1–11. http://dx.doi.org/10.1155/2019/6035870.

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The maximum likelihood (ML) method achieves an excellent performance for DOA estimation. However, its computational complexity is too high for a multidimensional nonlinear solution search. To address this issue, an improved bee evolutionary genetic algorithm (IBEGA) is applied to maximize the likelihood function for DOA estimation. First, an opposition-based reinforcement learning method is utilized to achieve a better initial population for the BEGA. Second, an improved arithmetic crossover operator is proposed to improve the global searching performance. The experimental results show that th

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14

Ramadan, Saleem Z. "A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box." Modern Applied Science 10, no. 2 (2016): 67. http://dx.doi.org/10.5539/mas.v10n2p67.

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<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed infor

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15

Langdon, William B. "Dissipative Arithmetic." Complex Systems 31, no. 3 (2022): 287–309. http://dx.doi.org/10.25088/complexsystems.31.3.287.

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Large arithmetic expressions are dissipative: they lose information and are robust to perturbations. Lack of conservation gives resilience to fluctuations. The limited precision of floating point and the mixture of linear and nonlinear operations make such functions anti-fragile and give a largely stable locally flat plateau a rich fitness landscape. This slows long-term evolution of complex programs, suggesting a need for depth-aware crossover and mutation operators in tree-based genetic programming. It also suggests that deeply nested computer program source code is error tolerant because di

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16

Choi, Yoon, Jingeun Kim, and Yourim Yoon. "A Comparison of Binary and Integer Encodings in Genetic Algorithms for the Maximum k-Coverage Problem with Various Genetic Operators." Biomimetics 10, no. 5 (2025): 274. https://doi.org/10.3390/biomimetics10050274.

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The maximum k-coverage problem (MKCP) is a problem of finding a solution that includes the maximum number of covered rows by selecting k columns from an m ×n matrix of 0s and 1s. This is an NP-hard problem that is difficult to solve in a realistic time; therefore, it cannot be solved with a general deterministic algorithm. In this study, genetic algorithms (GAs), an evolutionary arithmetic technique, were used to solve the MKCP. Genetic algorithms (GAs) are meta-heuristic algorithms that create an initial solution group, select two parent solutions from the solution group, apply crossover and

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17

Amen, Al-khafaji. "Multi-Operator Genetic Algorithm for Dynamic Optimization Problems." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 3 (2017): 139. http://dx.doi.org/10.11591/ijai.v6.i3.pp139-142.

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<span lang="EN-US">Maintaining population diversity is the most notable challenge in solving dynamic optimization problems (DOPs). Therefore, the objective of an efficient dynamic optimization algorithm is to track the optimum in these uncertain environments, and to locate the best solution. In this work, we propose a framework that is based on multi operators embedded in genetic algorithms (GA) and these operators are heuristic and arithmetic crossovers operators. The rationale behind this is to address the convergence problem and to maintain the diversity. The performance of the propos

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18

Grobler, Jacomine, and Andries P. Engelbrecht. "Arithmetic and parent-centric headless chicken crossover operators for dynamic particle swarm optimization algorithms." Soft Computing 22, no. 18 (2017): 5965–76. http://dx.doi.org/10.1007/s00500-017-2917-8.

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19

Carmona Cortes, Omar Andres, and Josenildo Costa da Silva. "Unconstrained numerical optimization using real-coded genetic algorithms: a study case using benchmark functions in R from Scratch." Revista Brasileira de Computação Aplicada 11, no. 3 (2019): 1–11. http://dx.doi.org/10.5335/rbca.v11i3.9047.

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Unconstrained numerical problems are common in solving practical applications that, due to its nature, are usually devised by several design variables, narrowing the kind of technique or algorithm that can deal with them. An interesting way of tackling this kind of issue is to use an evolutionary algorithm named Genetic Algorithm. In this context, this work is a tutorial on using real-coded genetic algorithms for solving unconstrained numerical optimization problems. We present the theory and the implementation in R language. Five benchmarks functions (Rosenbrock, Griewank, Ackley, Schwefel, a

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20

Jiang, Ming, Haihan Yu, and Jiaqing Chen. "Improved Self-Learning Genetic Algorithm for Solving Flexible Job Shop Scheduling." Mathematics 11, no. 22 (2023): 4700. http://dx.doi.org/10.3390/math11224700.

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The flexible job shop scheduling problem (FJSP), one of the core problems in the field of generative manufacturing process planning, has become a hotspot and a challenge in manufacturing production research. In this study, an improved self-learning genetic algorithm is proposed. The single mutation approach of the genetic algorithm was improved, while four mutation operators were designed on the basis of process coding and machine coding; their weights were updated and their selection mutation operators were adjusted according to the performance in the iterative process. Combined with the impr

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21

Tebbal, Ibtissam, and Abdelhak Ferhat Hamida. "Effects of Crossover Operators on Genetic Algorithms for the Extraction of Solar Cell Parameters from Noisy Data." Engineering, Technology & Applied Science Research 13, no. 3 (2023): 10630–37. http://dx.doi.org/10.48084/etasr.5417.

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This study analyzed the accuracy of solar cell modeling parameters extracted from noisy data using Genetic Algorithms (GAs). Three crossover operators (XOs) were examined, namely the Uniform (UXO), Arithmetic (AXO), and Blend (BXO) operators. The data used were an experimental benchmark cell and a simulated curve where noise levels (p) from 0 to 10% were added. For each XO, the analysis was carried out by running GAs 100 times and varying p and population size (Npop). Simulation results showed that UXO and AXO suffered from premature convergence and failed to provide parameters with good preci

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22

Zhu, Chendi, Lujun Li, Yuli Wu, and Zhengxing Sun. "SasWOT: Real-Time Semantic Segmentation Architecture Search WithOut Training." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7722–30. http://dx.doi.org/10.1609/aaai.v38i7.28606.

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In this paper, we present SasWOT, the first training-free Semantic segmentation Architecture Search (SAS) framework via an auto-discovery proxy. Semantic segmentation is widely used in many real-time applications. For fast inference and memory efficiency, Previous SAS seeks the optimal segmenter by differentiable or RL Search. However, the significant computational costs of these training-based SAS limit their practical usage. To improve the search efficiency, we explore the training-free route but empirically observe that the existing zero-cost proxies designed on the classification task are

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23

Li, Yong-Hua, Ziqiang Sheng, Pengpeng Zhi, and Dongming Li. "Multi-objective optimization design of anti-rolling torsion bar based on modified NSGA-III algorithm." International Journal of Structural Integrity ahead-of-print, ahead-of-print (2019). http://dx.doi.org/10.1108/ijsi-03-2019-0018.

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Purpose How to get a lighter and stronger anti-rolling torsion bar has become a barrier for the development of high-speed railway vehicles. The purpose of this paper is to realize the multi-objective optimization of an anti-rolling torsion bar with a Modified Non-dominated Sorting Genetic Algorithm III (MNSGA-III), which aims to obtain a better design scheme of an anti-rolling torsion bar device. Design/methodology/approach First, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) uses a simulated binary crossover (SBX) operator and a polynomial mutation operator, while the MNSGA-III a

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24

Yao, Lizhong, Qian Fan, Lei Zhao, Yanyan Li, and Qingping Mei. "Establishing the energy consumption prediction model of aluminum electrolysis process by genetically optimizing wavelet neural network." Frontiers in Energy Research 10 (September 13, 2022). http://dx.doi.org/10.3389/fenrg.2022.1009840.

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Nowadays, it is very popular to employ genetic algorithm (GA) and its improved strategies to optimize neural networks (i.e., WNN) to solve the modeling problems of aluminum electrolysis manufacturing system (AEMS). However, the traditional GA only focuses on restraining the infinite growth of the optimal species without reducing the similarity among the remaining excellent individuals when using the exclusion operator. Additionally, when performing arithmetic crossover or Cauchy mutation, a functional operator that conforms to the law of evolution is not constructed to generate proportional co

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Alsaidi, Ali H., Wedad Al-Sorori, and Abdulqader M. Mohsen. "DNCCLA: Discrete New Caledonian Crow Learning Algorithm for Solving Traveling Salesman Problem." Applied Computational Intelligence and Soft Computing 2024, no. 1 (2024). https://doi.org/10.1155/acis/5324998.

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The development of metaheuristic algorithms has led to the solution of various optimization problems. Bioinspired optimization algorithms like the New Caledonian crow learning algorithm (NCCLA) are primarily designed to address continuous problems. As most real‐world problems are discrete, some operators have been proposed to convert continuous algorithms into discrete ones to address these problems. These operators include evolutionary operators such as crossover and mutation, transformation operators such as symmetry, swap, and shift, and K‐opt algorithms such as 2‐opt, 2‐opt and a half, and

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