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Evaluation Model of Product Shape Design Scheme Based on Fuzzy Genetic Algorithm Mining Spatial Association Rules

Jie Wu, Gengxin Sun
2022 Mathematical Problems in Engineering  
Put forward a kind of association rules mining method based on fuzzy genetic algorithm, this approach by building a mining model, the association rules and fuzzy genetic algorithm fuses in together, and  ...  structure of multidimensional optimization problem is solved.  ...  , and fuzzy genetic algorithm is slightly better than genetic algorithm and particle swarm optimization.  ... 
doi:10.1155/2022/2888472 fatcat:6wyocvgtenhtvpbtkkvrnet2ia

Clustered Genetic Algorithm to solve Multidimensional Knapsack Problem

Dr. Prabha Shreeraj Nair
2017 International Journal of Trend in Scientific Research and Development  
Genetic Algorithm (GA) has emerged as a powerful tool to discover optimal for multidimensional knapsack problem (MDKP).  ...  Clustered genetic algorithm consist of (1) fuzzy roulette wheel selection for individual selection to form the mating pool (2) A different kind of crossover operator which employ hierarchical clustering  ...  CONCLUSION AND FUTURE WORK This paper addressed multidimensional knapsack problem via the cluster genetic algorithm to find optimal.  ... 
doi:10.31142/ijtsrd2237 fatcat:ecp3antnybbuvpfb6wklmkmrs4

Forecasting the Direction of Short-Term Crude Oil Price Changes with Genetic-Fuzzy Information Distribution

Xinyu Wang, Kegui Chen, Xueping Tan
2018 Mathematical Problems in Engineering  
In this approach, the short-term oil price series is associated with incomplete fuzzy information, and a new fused genetic-fuzzy information distribution method is developed to process such a fuzzy incomplete  ...  information set; then a feasible coding method of multidimensional information controlling points is adopted to fit genetic-fuzzy information distribution to time series forecasting.  ...  The genetic algorithm is used to optimize the assignment of fuzzy information controlling points, and a coding algorithm of multidimensional information controlling points is applied to make genetic-fuzzy  ... 
doi:10.1155/2018/3868923 fatcat:xaoxzv7ewragfhlbki3srtj55i

An Interactive Fuzzy Satisficing Method for Multiobjective Nonlinear Integer Programming Problems with Block-Angular Structures through Genetic Algorithms with Decomposition Procedures

Masatoshi Sakawa, Kosuke Kato
2009 Advances in Operations Research  
Realizing the block-angular structures that can be exploited in solving problems, we also propose genetic algorithms with decomposition procedures.  ...  We focus on multiobjective nonlinear integer programming problems with block-angular structures which are often seen as a mathematical model of large-scale discrete systems optimization.  ...  Furthermore, they proposed genetic algorithms with double strings using linear programming relaxation GADSLPR 16 for multiobjective multidimensional integer knapsack problems and genetic algorithms with  ... 
doi:10.1155/2009/372548 fatcat:fn6lkio2ojgxnf4ksl2qnyqima

Genetic algorithms with decomposition procedures for multidimensional 0-1 knapsack problems with block angular structures

K. Kato, M. Sakawa
2003 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
This paper presents a detailed treatment of genetic algorithms with decomposition procedures as developed for large scale multidimensional 0-1 knapsack problems with block angular structures.  ...  Then genetic algorithms with decomposition procedures are presented as an approximate solution method for multidimensional 0-1 knapsack problems with block angular structures.  ...  Computational Procedures of the Genetic Algorithms When applying genetic algorithms with decomposition procedures to the problem (1), an approximate optimal solution of desirable precision must be obtained  ... 
doi:10.1109/tsmcb.2003.811126 pmid:18238188 fatcat:gih5go2ihvaopmiivwi4dht63y

A method of building type-2 fuzzy logic systems in multidimensional objects identification problems

Natalia Kondratenko, Olha Snihur
2017 Eastern-European Journal of Enterprise Technologies  
A genetic algorithm for training a type-2 fuzzy model with multiple inputs and outputs is introduced in [14] .  ...  In [14] , a genetic algorithm for tuning a fuzzy model in medical diagnostics is considered.  ...  P a s k a l e n k o Postgraduate student Department of Higher Mathematics* E-mail: *O. S.  ... 
doi:10.15587/1729-4061.2017.101635 fatcat:yftzixbjyvd6vjhj7saspwjxfq

On Multidimensional Linear Modelling Including Real Uncertainty

Jana Nowakova, Miroslav Pokorny
2014 Advances in Electrical and Electronic Engineering  
To identify the fuzzy coefficients of model the genetic algorithm is used. The linear approximation of vague function together with its possibility area are analytically and graphically expressed.  ...  In the paper are defined vague data as specialized fuzzy sets -fuzzy numbers and there is described a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague regression parameters  ...  Next, a fuzzy linear regression model as a fuzzy function with fuzzy numbers as vague parameters is identified using the genetic algorithms. 2.  ... 
doi:10.15598/aeee.v12i5.1143 fatcat:4oaitcurized7mefde7zf4i7hi

An improved genetic algorithm based approach to solve constrained knapsack problem in fuzzy environment

Chiranjit Changdar, G.S. Mahapatra, Rajat Kumar Pal
2015 Expert systems with applications  
In this paper, we have proposed an improved genetic algorithm (GA) to solve constrained knapsack problem in fuzzy environment.  ...  The genetic algorithm has been improved by introducing 'refining' and 'repairing' operations. Computational experiments with different randomly generated data sets are given in experiment section.  ...  On the other hand, a hybrid EDA-based algorithm have been developed byWang, Wang, and Xu (2012) to solve multidimensional KP.However, only a few papers have been published for fuzzy knapsack problem.  ... 
doi:10.1016/j.eswa.2014.09.006 fatcat:kettw3yk7ngffngpf7pn2uzqgy

Design of Multidimensional Classifiers using Fuzzy Brain Emotional Learning Model and Particle Swarm Optimization Algorithm

Yuan Sun, Chih-Min Lin
2021 Acta Polytechnica Hungarica  
This study presents a multidimensional classifier design using a fuzzy brain emotional learning model, combined with a particle swarm optimization (PSO) algorithm that allows a network to automatically  ...  The multidimensional fuzzy brain emotional learning classifier(MFBELC) is first described with corresponding fuzzy inference rules; then the PSO algorithm is applied for the optimum parameter choice.  ...  Then, a multidimensional fuzzy brain emotion learning classifier with reward signal optimization is developed.  ... 
doi:10.12700/aph.18.4.2021.4.2 fatcat:rneiquyryfegdoghtmu5pijqku

Application of Fuzzy Ant Colony Algorithm to Robotics Arm Inverse Kinematics Problem

Ming Zhao, Yong Dai
2016 ICIC Express Letters  
Fuzzy control ant colony algorithm makes a 'path-planning' on the gridded multidimensional domain of the function, which is transformed into a new multidimensional function optimization algorithm.  ...  The improvement of the conventional ant colony algorithm with fuzzy control intelligent pheromone updating method successfully increases convergence, accuracy and heuristic of the algorithm.  ...  However, genetic algorithm is essentially a non-heuristic optimization algorithm, and non-heuristic offspring cannot solve complex multi-DOF robot arm inverse kinematics problem in ideal.  ... 
doi:10.24507/icicel.10.01.43 fatcat:3de3kibzg5dxjbl2cli3qmewyu

Design of a hybrid intelligent system for the management of flood disaster risks

Oluwole Charles Akinyokun, Emem Etok Akpan, Udoinyang Godwin Inyang
2019 Artificial intelligence research  
This paper proposes a hybridized intelligent framework comprising fuzzy logic (FL), neural network and genetic algorithm for clustering and visualization of flood data, prediction and classification of  ...  A six-layered adaptive neuro-fuzzy inference system implementing mamdani's inference mechanism was design to evaluate input features based on fuzzy rules held in the multidimensional data model.  ...  Genetic algorithm model In Refs., [20, 21] the GA was employed to optimize only the connection weights of NN so that it could learn better.  ... 
doi:10.5430/air.v8n1p14 fatcat:s3visnifs5hzjp6r7zsn4bblju

Predictive Analysis of Economic Chaotic Time Series Based on Chaotic Genetics Combined with Fuzzy Decision Algorithm

Xiuge Tan, Wei Wang
2021 Complexity  
of chaotic genetics with fuzzy decision, introduced the basic principles of chaotic genetic algorithm and fuzzy decision algorithm, constructed a prediction model for economic chaotic time series, performed  ...  The study results of this paper provide a reference for further research on predictive analysis of economic chaotic time series based on chaotic genetics combined with fuzzy decision algorithm.  ...  combined with fuzzy decision, introduced the basic principles of chaotic genetic algorithm and fuzzy decision algorithm, constructed a prediction model for economic chaotic time series, performed parameter  ... 
doi:10.1155/2021/5517502 fatcat:ix3qqyos7bendfmtefzgc7qwva

Stochastic plans in SMEs: A novel multidimensional fuzzy logic system (mFLS) approach

Roberto Baeza Serrato
2018 Ingeniería e Investigación  
The aim of this paper is to propose a novel multidimensional stochastic Fuzzy Logic System (msFLS) approach to execute a plan with stochastic behavior in knitting SMEs and their evaluation.  ...  The fuzzy subsets or linguistic terms are labelled and categorized in a simple and clear language as poor (P), regular (R), good (G) and excellent (E).  ...  Each of these researches uses genetic algorithms for optimization of a deterministic sequence of work.  ... 
doi:10.15446/ing.investig.v38n2.65357 fatcat:m35muh2lz5hzpho5bk44wozkc4


Mukhamedieva D. T, Safarova L.U
2017 International Journal of Research in Engineering and Technology  
There is usually no opportunity to form simple sufficient symbolic-form models for complex processes defined as indeterminacy (inaccuracy, unstochasticity, incompleteness, fuzziness) in the background  ...  A compound part of ЕС -genetic algorithms are the algorithms of global optimization based on mechanisms of natural selection and genetics [8] .  ...  The fusion of the genetic algorithm with neural network gains the effective results as well.  ... 
doi:10.15623/ijret.2017.0609005 fatcat:j7lusinurrgjbaiicbphnvohmi

Heart Disease Prediction using Neuro-Genetic Algorithm and CNN-MDRP Classifier

Bhaskaru O., Sreedevi M.
2021 Indian Journal of Computer Science and Engineering  
The proposed neuro-genetic approach finds a feasible solution for optimal network configuration. The results prove that combining both effective algorithm and classifier acquires 96.25% of accuracy.  ...  To solve this problem, the proposed system designed a Neuro-Genetic with CNN-MDRP approach for heart disease prediction.  ...  Kernel[17] 76% Learning Vector Optimization[18] 77% Fuzzy Weighted AIRS[19] 81% NN with fuzzy membership function[20] 81% MLP with two hidden layers[21] 86% Neuro-Genetic approach[22] 90% ISAGSO-WFSVM  ... 
doi:10.21817/indjcse/2021/v12i4/211204145 fatcat:mg2hlgtswrbkvfnhvetufdtsge
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