Relevant bibliographies by topics / Arithmetic crossover operator

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Academic literature on the topic 'Arithmetic crossover operator'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Arithmetic crossover operator"

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

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Genetic Algorithm falls under the category of evolutionary algorithm that follows the principles of natural selection and genetics, where the best adapted individuals in a population are more likely to survive and reproduce, passing on their advantageous traits to their offsprings. Crossover is a crucial operator in genetic algorithms as it allows the genetic material of two or more individuals in the population to combine and create new individuals. Optimizing it can potentially lead to better solutions and faster convergence of the genetic algorithm. The proposed crossover operator gradually
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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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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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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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9

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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Conference papers on the topic "Arithmetic crossover operator"

1

De Carvalho Ribeiro Júnior, Egidio, Omar Andres Carmona Cortes, and Osvaldo Ronald Saavedra. "A Parallel Mix Self-Adaptive Genetic Algorithm for Solving the Dynamic Economic Dispatch Problem." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2499.

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The purpose of this paper is to propose a parallel genetic algorithm that has adaptive and self-adaptive characteristics at the same time for solving the Dynamic Economic Dispatch (DED) problem that is a challenging problem to solve. The algorithm selects the proper operators (using adaptive features) and probabilities (using the self-adaptive code) that produce the most fittable individuals. Regarding operations, the choice is made between four different types of crossover: simple, arithmetical, non-uniform arithmetical, and linear. Concerning mutation, we used four types of mutations (unifor
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