Fitness approximation for genetic algorithm using combination of approximation model and fuzzy clustering technique

2010 ◽  
Author(s):  
Jong-Won Yoon ◽  
Sung-Bae Cho
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Approximation Model ◽  
Clustering Technique ◽  
Fitness Approximation

scholarly journals Application of genetic and K-means algorithms in clustering Babakoohi Anticline joints north of Shiraz, Iran

2020 ◽  
Author(s):  
Hajar Kazemi ◽  
Kouros Yazdjerdi ◽  
Abdolmajid Asadi ◽  
Mohammad Reza Mozafari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Clustering ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Statistical Techniques ◽  
Clustering Technique ◽  
Primary Cluster

AbstractThe fuzzy clustering technique is one of the ways of organizing data that presents special patterns using algorithms and based on the similarity level of data. In this study, in order to cluster the resulting data from the Babakoohi Anticline joints, located north of Shiraz, K-means and genetic algorithms are applied. The K-means algorithm is one of the clustering algorithms easily implemented and of fast performance; however, sometimes this algorithm is located in the local optimal trap and cannot respond with an optimal answer, due to the sensitivity of this algorithm to the centers of the primary cluster. In addition, it has some basic disadvantages, such as its inappropriateness for complicated forms and also the dependency of the final result upon the primary cluster. Therefore, in order to perform the study more accurately and to obtain more reliable results, the genetic algorithm is used for categorizing the data of joints of the studied area. Applying this algorithm for leaving the local optimal points is an effective way. The results of clustering of the aforementioned data using the two above techniques represent two clusters in the Babakoohi Anticline. Furthermore, for validity and surveying of the results of the suggested techniques, various mathematical and statistical techniques, including ICC, Vw, VMPC, and VPMBF, are applied, which supports the similarity of the obtained results and the data clustering process in two algorithms.

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

scholarly journals Analysis of regional development disparities in Ukraine with fuzzy clustering technique

2019 ◽  
Vol 65 ◽  
pp. 04008
Author(s):  
Kateryna Gorbatiuk ◽  
Olha Mantalyuk ◽  
Oksana Proskurovych ◽  
Oleksandr Valkov
Keyword(s):  
Fuzzy Clustering ◽  
Maximum Growth ◽  
Data Sets ◽  
Economic Policies ◽  
Clustering Methods ◽  
Economic Activities ◽  
Development Indicators ◽  
Clustering Technique ◽  
Regional Cluster ◽  
Partition Clustering

Disparities in the development of regions in any country affect the entire national economy. Detecting the disparities can help formulate the proper economic policies for each region by taking action against the factors that slow down the economic growth. This study was conducted with the aim of applying clustering methods to analyse regional disparities based on the economic development indicators of the regions of Ukraine. There were considered fuzzy clustering methods, which generalize partition clustering methods by allowing objects to be partially classified into more than one cluster. Fuzzy clustering technique was applied using R packages to the data sets with the statistic indicators concerned to the economic activities in all administrative regions of Ukraine in 2017. Sets of development indicators for different sectors of economic activity, such as industry, agriculture, construction and services, were reviewed and analysed. The study showed that the regional cluster classification results strongly depend on the input development indicators and the clustering technique used for this purpose. Consideration of different partitions into fuzzy clusters opens up new opportunities in developing recommendations on how to differentiate economic policies in order to achieve maximum growth for the regions and the entire country.

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