Relevant bibliographies by topics / REAL CODED GENETIC ALGORITHM (RCGA)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'REAL CODED GENETIC ALGORITHM (RCGA)'

Author: Grafiati
Published: 26 October 2023
Last updated: 25 July 2025

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Journal articles on the topic "REAL CODED GENETIC ALGORITHM (RCGA)"

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Beklaryan, Gayane L., Andranik S. Akopov, and Nerses K. Khachatryan. "Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control." Cybernetics and Information Technologies 19, no. 2 (2019): 87–103. http://dx.doi.org/10.2478/cait-2019-0017.

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Abstract This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models (SD-models). The main feature of the genetic algorithm presented herein is the application of fuzzy control to its parameters, such as the probability of a mutation, type of crossover operator, size of the parent population, etc. The control rules for the Real-Coded Genetic Algorithm (RCGA) were suggested based on the estimation of the values of the performance metrics, such as rate of convergence, processing time and remoteness
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Akopov, Andranik S., Levon A. Beklaryan, and Armen L. Beklaryan. "Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation." Cybernetics and Information Technologies 21, no. 3 (2021): 127–44. http://dx.doi.org/10.2478/cait-2021-0034.

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Abstract This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been e
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Mahmudy, Wayan F., Romeo M. Marian, and Lee H. S. Luong. "Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization." Advanced Materials Research 701 (May 2013): 364–69. http://dx.doi.org/10.4028/www.scientific.net/amr.701.364.

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This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple gen
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Uemura, Kento, and Isao Ono. "AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 3 (2016): 429–37. http://dx.doi.org/10.20965/jaciii.2016.p0429.

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This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adap
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Selvakumari Jeya, I. Jasmine, and S. N. Deepa. "Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier." Computational and Mathematical Methods in Medicine 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7493535.

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A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused o
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Cherif, Imen, and Farhat Fnaiech. "Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)." International Journal of Applied Mathematics and Computer Science 25, no. 4 (2015): 863–75. http://dx.doi.org/10.1515/amcs-2015-0062.

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Abstract This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method
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Shabalin, Denis, and Vladimir Stanovov. "Neural network-based vehicle control in simulated environments using real-coded genetic algorithms." ITM Web of Conferences 72 (2025): 05008. https://doi.org/10.1051/itmconf/20257205008.

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This research explores a method of optimizing neural networks for vehicle control in a simulation environment using a real-coded genetic algorithm (RCGA). The study focuses on applying RCGA in conjunction with multiple genetic operators, including simulated binary crossover (SBX), power mutation (PM), and tournament selection, to evolve neural network weights and biases, enhancing control performance for simulated vehicles. By utilizing RCGA to adjust neural network parameters, the approach enables adaptive and efficient vehicle control. The experiments demonstrate that combining sensor data w
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Akopov, Andranik S., Levon A. Beklaryan, and Armen L. Beklaryan. "Cluster-Based Optimization of an Evacuation Process Using a Parallel Bi-Objective Real-Coded Genetic Algorithm." Cybernetics and Information Technologies 20, no. 3 (2020): 45–63. http://dx.doi.org/10.2478/cait-2020-0027.

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AbstractThis work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were c
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Nakane, Takumi, Xuequan Lu, and Chao Zhang. "A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm." Computational Intelligence and Neuroscience 2020 (September 27, 2020): 1–20. http://dx.doi.org/10.1155/2020/8835852.

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In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, t
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Padmanabhan, S., M. Chandrasekaran, P. Asokan, and V. Srinivasa Raman. "A Performance Study of Real Coded Genetic Algorithm on Gear Design Optimization." Advanced Materials Research 622-623 (December 2012): 64–68. http://dx.doi.org/10.4028/www.scientific.net/amr.622-623.64.

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he major problem that deals with practical engineers is the mechanical design and creativeness. Mechanical design can be defined as the choice of materials and geometry, which satisfies, specified functional requirements of that design. A good design has to minimize the most significant adverse result and to maximize the most significant desirable result. An evolutionary algorithm offers efficient ways of creating and comparing a new design solution in order to complete an optimal design. In this paper a type of Genetic Algorithm, Real Coded Genetic Algorithm (RCGA) is used to optimize the des
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Dissertations / Theses on the topic "REAL CODED GENETIC ALGORITHM (RCGA)"

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KUMAR, NEERAJ. "DESIGNING OF MARKET MODEL, EFFECTIVE PRICE FORECASTING TOOL AND BIDDING STRATEGY FOR INDIAN ELECTRICITY MARKET." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18910.

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The Development scenario for renewable energy across the globe is changing rapidly in terms of capacity addition and grid interconnection. Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power growing enormously across the globe. Solar energy is widely escalating in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity b
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Dyer, John David Hartfield Roy J. "Aerospace design optimization using a real coded genetic algorithm." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Aerospace_Engineering/Thesis/Dyer_John_31.pdf.

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Hussain, M. S. "Real-coded genetic algorithm particle filters for high-dimensional state spaces." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1426733/.

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This thesis successfully addresses the issues faced by particle filters in high-dimensional state-spaces by comparing them with genetic algorithms and then using genetic algorithm theory to address these issues. Sequential Monte Carlo methods are a class of online posterior density estimation algorithms that are suitable for non-Gaussian and nonlinear environments, however they are known to suffer from particle degeneracy; where the sample of particles becomes too sparse to approximate the posterior accurately. Various techniques have been proposed to address this issue but these techniques fa
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Phan, Thanh Duoc. "Design optimisation of steel portal frames using real-coded niching genetic algorithm." Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.602785.

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This thesis is concerned with the design optimization of single-storey steel portal frame buildings. In the UK, such buildings account for 90% of all single-storey buildings and 50% of all constructional steelwork used. Two different types of steel portal frames are considered: conventional hot-rolled steel portal frames, which can achieve spans of up to 50 m, and cold-formed steel frames, which while less popular in the UK, may be more efficient for spans around 12 m. A real-coded niching genetic algorithm is used for the purposes of the design optimization. By adopting a niching strategy, th
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Lao, Seng Kin. "Computer-aided analysis for combined building services drawings using Real-coded Genetic Algorithm." Thesis, University of Macau, 2001. http://umaclib3.umac.mo/record=b1446119.

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Darwish, Mohammed. "Lot-sizing and scheduling optimization using genetic algorithm." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17045.

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Simultaneous lot-sizing and scheduling problem is the problem to decide what products to be produced on which machine and in which order, as well as the quantity of each product. Problems of this type are hard to solve. Therefore, they were studied for years, and a considerable number of papers is published to solve different lotsizing and scheduling problems, specifically real-case problems. This work proposes a Real-Coded Genetic Algorithm (RCGA) with a new chromosome representation to solve a non-identical parallel machine capacitated lot-sizing and scheduling problem with sequence dependen
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Lin, Chun-Hung, and 林俊宏. "Adaptive Real-coded Genetic Algorithm for Motor System Identification." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/35255717604174019114.

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碩士<br>國立高雄第一科技大學<br>電機工程研究所碩士班<br>102<br>In this paper, the main objective is to identify the parameters of motors, which includes a BLDC motor and an induction motor. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and adaptive genetic algorithm (ARGA) are compared in the rotational angular speeds and fitness values, which are the inverse of square difference of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the
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Gibbs, Matthew S. "Real-coded genetic algorithm parameter setting for water distribution system optimisation." Thesis, 2008. http://hdl.handle.net/2440/49644.

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The management of Water Distribution Systems (WDSs) involves making decisions about various operations in the network, including the scheduling of pump operations and setting of disinfectant dosing rates. There are often conflicting objectives in making these operational decisions, such as minimising costs while maximising the quality of the water supplied. Hence, the operation of WDSs can be very difficult, and there is generally considerable scope to improve the operational efficiency of these systems by improving the associated decision making process. In order to achieve this goal, o
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Gibbs, Matthew S. "Real-coded genetic algorithm parameter setting for water distribution system optimisation." 2008. http://hdl.handle.net/2440/49644.

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The management of Water Distribution Systems (WDSs) involves making decisions about various operations in the network, including the scheduling of pump operations and setting of disinfectant dosing rates. There are often conflicting objectives in making these operational decisions, such as minimising costs while maximising the quality of the water supplied. Hence, the operation of WDSs can be very difficult, and there is generally considerable scope to improve the operational efficiency of these systems by improving the associated decision making process. In order to achieve this goal, optimis
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Kung, Yen-Shiung, and 龔彥勲. "Motion planning for redundant robots using a CUDA-based real-coded genetic algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/7982t2.

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碩士<br>國立臺北科技大學<br>自動化科技研究所<br>100<br>Motion planning of redundant robot manipulators is a difficult problems encountered in the field of robotics. In this paper, we use Forward Kinematic and Optimal Control to avoid singularity problem in motion planning that use Inverse Kinematic to compute. It is a large quantity of computing in searching optimal result process, due to this reason, we use Genetic Algorithm and Particle Swarm Optimization Algorithm to reduce complexity and use parallel computing architecture of GPU(CUDA) to improve computing speed.
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Book chapters on the topic "REAL CODED GENETIC ALGORITHM (RCGA)"

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Bhosale, K. C., and P. J. Pawar. "Material Flow Optimisation in a Manufacturing Plant by Real-Coded Genetic Algorithm (RCGA)." In Studies in Quantitative Decision Making. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5820-4_5.

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Wang, Zhonglai, Jingqi Xiong, Qiang Miao, Bo Yang, and Dan Ling. "New Hybrid Real-Coded Genetic Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_151.

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Chauhan, Pinkey. "Real Coded Genetic Algorithm for Selecting Optimal Machining Conditions." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1528-3_8.

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Munteanu, Cristian, and Vasile Lazarescu. "Improving Mutation Capabilities in a Real-Coded Genetic Algorithm." In Evolutionary Image Analysis, Signal Processing and Telecommunications. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10704703_11.

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Devi Arockia Vanitha, C., D. Devaraj, and M. Venkatesulu. "Real Coded Genetic Algorithm for Development of Optimal G-K Clustering Algorithm." In Swarm, Evolutionary, and Memetic Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_23.

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Singh, Prabhash Kumar. "A Modified Real-Coded Extended Line Crossover for Genetic Algorithm." In Social Transformation – Digital Way. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1343-1_58.

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Nadakuditi, Gouthamkumar, U. Mohan Rao, Venkateswararao Bathina, and Srihari Pandi. "Economic Load Dispatch in Microgrids Using Real-Coded Genetic Algorithm." In Soft Computing in Data Analytics. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_38.

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Hamada, Naoki, Jun Sakuma, Shigenobu Kobayashi, and Isao Ono. "Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA." In Parallel Problem Solving from Nature – PPSN X. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87700-4_69.

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Li, Xiaodong. "A Real-Coded Predator-Prey Genetic Algorithm for Multiobjective Optimization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36970-8_15.

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Deep, Kusum, Shashi, and V. K. Katiyar. "A New Real Coded Genetic Algorithm Operator: Log Logistic Mutation." In Advances in Intelligent and Soft Computing. Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0487-9_19.

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Conference papers on the topic "REAL CODED GENETIC ALGORITHM (RCGA)"

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Ghasemian, Amir, Sobhan Mohamadian, Concettina Buccella, Maria Gabriella Cimoroni, and Carlo Cecati. "Selective Harmonic Mitigation of Cascaded H-Bridge Multilevel Converters Using Real-Coded Genetic Algorithm." In 2024 IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE). IEEE, 2024. https://doi.org/10.1109/icaige62696.2024.10776685.

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Singh, Gurjot, Neeraj Gupta, and Mahdi Khosravy. "New crossover operators for real coded genetic algorithm (RCGA)." In 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2015. http://dx.doi.org/10.1109/iciibms.2015.7439507.

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Minuchehr, Hamid, Ahmad Zolfaghari, Peymaan Makarachi, and Ali Noroozy. "Using a Real Coded Genetic Algorithm to Obtain the Optimal Parameters of a Cascade of Gas Centrifuge." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75926.

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A minimum total number of centrifuges can be taken as an optimization criterion in designing a cascade of centrifuges. The separable power of a centrifuge of given geometry (rotor height and radius) and specified peripheral speed (the highest that materials of construction can withstand) depends upon the internal variables controlling the component drives and upon the operating variables, which are the cut θ, and the throughput, L. To enhance separative power of a gas centrifuges cascade, one needs to optimize all parameters. In this paper, the Real Coded Genetic Algorithm, RCGA, is implemente
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Shi, Zhen, and Peter Sandborn. "Modeling Test, Diagnosis, and Rework Operations and Optimizing Their Location in General Manufacturing Processes." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dfm-48145.

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This paper presents a test, diagnosis, and rework analysis model for use in manufacturing process modeling. The approach includes a model of functional test operations characterized by fault coverage, false positives, and defects introduced in test, in addition to rework and diagnosis (diagnostic test) operations that have variable success rates and their own defect introduction mechanisms. The model accommodates multiple rework attempts on a product instance. The model is applied within a framework for optimizing the location(s) and characteristics (fault coverage/test cost, rework success ra
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Chakraborty, S., S. Phadke, and R. K. Verma. "AVA Inversion Using Real-Coded Genetic Algorithm." In 66th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2004. http://dx.doi.org/10.3997/2214-4609-pdb.3.p307.

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Turnea, Marius, Dragos Arotaritei, and Robert Fuior. "INTELLIGENT ALGORITHMS WITH SELECTION OF HYPERPARAMETERS FOR E-HEALTH APPLICATIONS POWERED BY 5G WIRELESS NETWORKS." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-205.

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Different from previous generations, the 5G networks has new capabilities due to service-based architecture model and virtualization. The successful broadband networks must be able to handle the growth in the data traffic. The e-Health networks has additional issues as the continuous monitoring of patients suffering from chronic diseases (non-communicable diseases). The wearable devices used for monitoring are supposed to be used for balneo-physio-kinetotherapy (including the body gait index calculation) in the future and this will require and increasing traffic as users and data for 5G networ
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Kumar, Sanjeev, Manoj Thakur, Balasubramanian Raman, and N. Sukavanam. "Stereo camera calibration using real coded genetic algorithm." In TENCON 2008 - 2008 IEEE Region 10 Conference (TENCON). IEEE, 2008. http://dx.doi.org/10.1109/tencon.2008.4766590.

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Zhang, Guo-li, Geng-yin Li, and Jian-wei Ma. "Real-Coded Genetic Algorithm for Constrained Optimization Problem." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.259005.

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HajAbedi, Z. "A real-coded genetic algorithm for constructive induction." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983191.

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Chen Lan and Li Fei. "A real-coded chaotic immune quantum genetic algorithm." In 2010 International Conference on Future Information Technology and Management Engineering (FITME). IEEE, 2010. http://dx.doi.org/10.1109/fitme.2010.5655686.

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