Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Algorithms for clustering data
Algorithms for clustering data
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
A variable-length genetic algorithm for clustering and classification
Pattern Recognition Letters - Special issue on genetic algorithms
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
On finding the number of clusters
Pattern Recognition Letters
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms
Evolutionary Computation
A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-means Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters
IEEE Transactions on Knowledge and Data Engineering
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Where Are the Niches? Dynamic Fitness Sharing
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Gaussian mixture density modeling, decomposition, and applications
IEEE Transactions on Image Processing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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In this paper, a genetic clustering algorithm based on dynamic niching with niche migration (DNNM-clustering) is proposed. It is an effective and robust approach to clustering on the basis of a similarity function relating to the approximate density shape estimation. In the new algorithm, a dynamic identification of the niches with niche migration is performed at each generation to automatically evolve the optimal number of clusters as well as the cluster centers of the data set without invoking cluster validity functions. The niches can move slowly under the migration operator which makes the dynamic niching method independent of the radius of the niches. Compared to other existing methods, the proposed clustering method exhibits the following robust characteristics: (1) robust to the initialization, (2) robust to clusters volumes (ability to detect different volumes of clusters), and (3) robust to noise. Moreover, it is free of the radius of the niches and does not need to pre-specify the number of clusters. Several data sets with widely varying characteristics are used to demonstrate its superiority. An application of the DNNM-clustering algorithm in unsupervised classification of the multispectral remote sensing image is also provided.