scholarly journals GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yanhua Wang ◽  
Xiyu Liu ◽  
Laisheng Xiang
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Membrane System ◽  
P System ◽  
Data Sets ◽  
Ensemble Clustering ◽  
Improved Genetic Algorithm ◽  
Robust Clustering

Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.

Application on Query of Distributed Database Based on Improved Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4617-4621
Author(s):  
Fu Xing Chen ◽  
Xu Sheng Xie
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Distributed Database ◽  
Improved Genetic Algorithm ◽  
Mutation Probability ◽  
Probabilistic Search ◽  
Crossover Probability ◽  
Crossover And Mutation

The query cost usually as an important criterion for a distributed database. The genetic algorithm is an adaptive probabilistic search algorithm, but the crossover and mutation probability usually keep a probability in traditional genetic algorithm. If the crossover probability keep a large value, the possibility of damage for genetic algorithm model is greater; In turn, if the crossover probability keep a small value, the search process will transform a slow processing or even stagnating. If the mutation probability keep a small value, a new individual can be reproduced difficultly; In turn, if the mutation probability keep a large value, the genetic algorithm will as a Pure random search algorithm. To solve this problem, proposed a improved genetic algorithm that multiple possibility of crossover and mutation based on k-means clustering algorithm. The experiment results indicate that the algorithm is effective.

scholarly journals An Adaptive Ellipse Distance Density Peak Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4920
Author(s):  
Lin Cao ◽  
Xinyi Zhang ◽  
Tao Wang ◽  
Kangning Du ◽  
Chong Fu
Keyword(s):  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Search Algorithm ◽  
Measurement Data ◽  
Data Sets ◽  
Density Peak ◽  
Close Range ◽  
Decision Graph ◽  
Membership Matrix

In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. In response to this problem, this paper proposes a new clustering algorithm, namely an adaptive ellipse distance density peak fuzzy (AEDDPF) clustering algorithm. Firstly, the Euclidean distance is replaced by adaptive ellipse distance, which can more accurately describe the structure of data obtained by radar measurement vehicles. Secondly, the adaptive exponential function curve is introduced in the decision graph of the fast density peak search algorithm to accurately select the density peak point, and the initialization of the AEDDPF algorithm is completed. Finally, the membership matrix and the clustering center are calculated through successive iterations to obtain the clustering result.The time complexity of the AEDDPF algorithm is analyzed. Compared with the density-based spatial clustering of applications with noise (DBSCAN), k-means, fuzzy c-means (FCM), Gustafson-Kessel (GK), and adaptive Euclidean distance density peak fuzzy (Euclid-ADDPF) algorithms, the AEDDPF algorithm has higher clustering accuracy for real measurement data sets in certain scenarios. The experimental results also prove that the proposed algorithm has a better clustering effect in some close-range vehicle scene applications. The generalization ability of the proposed AEDDPF algorithm applied to other types of data is also analyzed.

A Dynamic Genetic Algorithm for Clustering Problems

2013 ◽  
Vol 411-414 ◽  
pp. 1884-1893
Author(s):  
Yong Chun Cao ◽  
Ya Bin Shao ◽  
Shuang Liang Tian ◽  
Zheng Qi Cai
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Search Space ◽  
Adaptive Mutation ◽  
Data Sets ◽  
Data Set ◽  
Local Optima ◽  
Clustering Problems

Due to many of the clustering algorithms based on GAs suffer from degeneracy and are easy to fall in local optima, a novel dynamic genetic algorithm for clustering problems (DGA) is proposed. The algorithm adopted the variable length coding to represent individuals and processed the parallel crossover operation in the subpopulation with individuals of the same length, which allows the DGA algorithm clustering to explore the search space more effectively and can automatically obtain the proper number of clusters and the proper partition from a given data set; the algorithm used the dynamic crossover probability and adaptive mutation probability, which prevented the dynamic clustering algorithm from getting stuck at a local optimal solution. The clustering results in the experiments on three artificial data sets and two real-life data sets show that the DGA algorithm derives better performance and higher accuracy on clustering problems.

scholarly journals HYBRID NSGA-II OPTIMIZATION FOR IMPROVING THE THREE-TERM BP NETWORK FOR MULTICLASS CLASSIFICATION PROBLEMS

2015 ◽  
Author(s):  
Ashraf Osman Ibrahim ◽  
Siti Mariyam Shamsuddin ◽  
Sultan Noman Qasem
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Classification Performance ◽  
Classification Problems ◽  
Nsga Ii ◽  
Bp Network ◽  
Multiobjective Evolutionary Algorithm

Recently, hybrid algorithms have received considerable attention from a number of researchers. This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions. The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions. The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. The outcome positively demonstrates that the hybrid algorithm is able to improve the classification performance with a smaller number of hidden nodes and is effective in multiclass classifi cation problems. Furthermore, the results indicate that the proposed hybrid method is a potentially useful classifi er for enhancing the classification process ability when compared with the multiobjective genetic algorithm based on the TBP network (MOGATBP) and certain other methods found in the literature.  

A Novel Membrane Clustering Algorithm Based on Tissue-like P System

2016 ◽  
Vol 2 (2) ◽  
pp. 409
Author(s):  
Yan Huaning ◽  
Xiang Laisheng ◽  
Liu Xiyu ◽  
Xue Jie
Keyword(s):  
Clustering Algorithm ◽  
Membrane Computing ◽  
P System ◽  
Data Sets ◽  
Turing Machines ◽  
Computing Systems ◽  
Swarm Optimization ◽  
Computing Model ◽  
Clustering Quality ◽  
Data Points

<span lang="EN-US">Clustering is a process of partitioning data points into different clusters due to their similarity, as a powerful technique of data mining, clustering is widely used in many fields. Membrane computing is a computing model abstracting from the biological area, </span><span lang="EN-US">these computing systems are proved to be so powerful that they are equivalent with Turing machines. In this paper, a modified inversion particle swarm optimization was proposed, this method and the mutational mechanism of genetics algorithm were used to combine with the tissue-like P system, through these evolutionary algorithms and the P system, the idea of a novel membrane clustering algorithm could come true. Experiments were tested on six data sets, by comparing the clustering quality with the GA-K-means, PSO-K-means and K-means proved the superiority of our method.</span>

ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS

2007 ◽  
Vol 16 (01) ◽  
pp. 111-120 ◽  
Author(s):  
MANISH MANGAL ◽  
MANU PRATAP SINGH
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Search Algorithm ◽  
Random Search ◽  
Feedforward Neural Networks ◽  
Feedforward Neural Network ◽  
Data Sets ◽  
Classification Problems ◽  
Random Search Algorithm

This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested.

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