scholarly journals A Proposal of Genetic Algorithm with a Local Improvement Mechanism and Finding of Fuzzy Rules

1995 ◽  
Vol 7 (5) ◽  
pp. 978-987 ◽  
Author(s):  
Takeshi FURUHASHI ◽  
Ken NAKAOKA ◽  
Hiroshi MAEDA ◽  
Yoshiki UCHIKAWA
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Rules ◽  
Local Improvement

Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

2020 ◽  
Vol 17 (6) ◽  
pp. 2755-2762
Author(s):  
Pramoda Patro ◽  
Krishna Kumar ◽  
G. Suresh Kumar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Problem ◽  
Fuzzy Neural Network ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Fuzzy Neural ◽  
Approximate Function ◽  
Age Prediction

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

scholarly journals A Fast Model Based on Genetic Algorithm to Construct Fuzzy Rules

2020 ◽  
Vol 9 (2) ◽  
pp. 449-456
Keyword(s):  
Genetic Algorithm ◽  
Genetic Model ◽  
Fuzzy Rule ◽  
Database Systems ◽  
Fuzzy Rules ◽  
Multiobjective Evolutionary Algorithms ◽  
Fuzzy Decision ◽  
Model Based ◽  
Selection Approach ◽  
Nondominated Sorting

Fuzzy rule has been used extensively in data mining. This paper presents a fast and flexible method based on genetic algorithm to construct fuzzy decision rule with considering criteria of accuracy. First, the algorithm determines the width that divides each attribute into “n” intervals according to the number of fuzzy sets, after that calculates the parameters width according to that width. Rough Sets Model Based on Database Systems technique used to reduce the number of attributes if there exists then we use the algorithm for extracting initial fuzzy rules from fuzzy table using SQL statements with a smaller number of rules than the other models without needing to use a genetic algorithm – Based Rule Selection approach to select a small number of significant rules, then it calculates their accuracy and the confidence.. Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for computational complexity and needing for specifying a sharing parameter but in our genetic model each fuzzy set represented by “Real number” from 0 to 9 forming a gene on chromosome (individual). Our genetic model is used to improve the accuracy of the initial rules and calculates the accuracy of the new rules again which be higher than the old rules The proposed approach is applied on the Iris dataset and the results compared with other models: Preselection with niches, ENORA and NSGA to show its validity.

Structure Organization of Hierarchical Fuzzy Model Using Genetic Algorithm

1995 ◽  
Vol 7 (1) ◽  
pp. 29-35
Author(s):  
Toshio Fukuda ◽  
◽  
Yasuhisa Hasegawa ◽  
Koji Shimojima
Keyword(s):  
Genetic Algorithm ◽  
Hierarchical Structure ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Numerical Data ◽  
Fuzzy Rules ◽  
Input And Output ◽  
The Hierarchical Structure ◽  
Input Variables ◽  
The Relationship

This paper proposes a method to organize the hierarchical structure of fuzzy model using the Genetic Algorithm and back-propagation method. The number of fuzzy rules increases exponentially with the number of input variables. Thus, a fuzzy system with many input variables has an extremely large number of fuzzy rules. Hierarchical structure of fuzzy reasoning is one of the methods to reduce the number of fuzzy rules and membership functions. However, it is very difficult to organize the hierarchical structure because the hierarchical structure cannot be constructed without considering the relationship among input and output variables. The proposed method can organize the suitable hierarchical structure for the relationship among input and output variables in teaching numerical data. It is based on the Genetic Algorithm with an evaluation function as a strategy that adopts a system with fewer fuzzy rules and more accurate outputs. The proposed method is applied to the approximation problems of multi-dimensional nonlinear functions in order to demonstrate its effectiveness.

Optimization of fuzzy rules design using genetic algorithm

2000 ◽  
Vol 31 (4) ◽  
pp. 251-262 ◽  
Author(s):  
S.V. Wong ◽  
A.M.S. Hamouda
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Rules
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