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30
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 90 (10 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Swarm Intelligence Algorithms for Data Clustering
 IN SOFT COMPUTING FOR KNOWLEDGE DISCOVERY AND DATA MINING BOOK, PART IV
"... Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge da ..."
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Cited by 26 (1 self)
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Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bioinspired algorithms, wellknown as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.
Automatic Clustering Using an Improved Differential Evolution Algorithm
, 2008
"... Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed ..."
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Cited by 23 (3 self)
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Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified. Rather, it determines the optimal number of partitions of the data “on the run. ” Superiority of the new method is demonstrated by comparing it with two recently developed partitional clustering techniques and one popular hierarchical clustering algorithm. The partitional clustering algorithms are based on two powerful wellknown optimization algorithms, namely the genetic algorithm and the particle swarm optimization. An interesting realworld application of the proposed method to automatic segmentation of images is also reported.
Document clustering into an unknown number of clusters using a genetic algorithm
 In: V. Matousek and P. Mautner (eds), International Conference on Text Speech and Dialogue TSD 2003, September 8–12, Ceské Budejovice, Czech Republic
, 2003
"... Abstract. We present a genetic algorithm that deals with document clustering. This algorithm calculates an approximation of the optimum k value, and solves the best grouping of the documents into these k clusters. We have evaluated this algorithm with sets of documents that are the output of a query ..."
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Cited by 13 (0 self)
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Abstract. We present a genetic algorithm that deals with document clustering. This algorithm calculates an approximation of the optimum k value, and solves the best grouping of the documents into these k clusters. We have evaluated this algorithm with sets of documents that are the output of a query in a search engine. The experiments show that, most of the times, our genetic algorithm obtains better values of the fitness function than the well known Calinski and Harabasz stopping rule, and takes less time. 1
Randomised Local Search Algorithm for the Clustering Problem
, 2000
"... : We consider clustering as a combinatorial optimisation problem. Local search provides a simple and effective approach to many ..."
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Cited by 7 (3 self)
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: We consider clustering as a combinatorial optimisation problem. Local search provides a simple and effective approach to many
Automatic Kernel Clustering with MultiElitist Particle Swarm Optimization Algorithm
"... Abstract — This article introduces a scheme for clustering complex and linearly nonseparable datasets, without any prior knowledge of the number of naturally occurring groups in the data. The proposed method is based on a modified version of classical Particle Swarm Optimization (PSO) algorithm, kn ..."
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Cited by 6 (1 self)
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Abstract — This article introduces a scheme for clustering complex and linearly nonseparable datasets, without any prior knowledge of the number of naturally occurring groups in the data. The proposed method is based on a modified version of classical Particle Swarm Optimization (PSO) algorithm, known as the Multielitist PSO (MEPSO) model. It also employs a kernelinduced similarity measure instead of the conventional sumofsquares distance. Use of the kernel function makes it possible to cluster data that is linearly nonseparable in the original input space into homogeneous groups in a transformed highdimensional feature space. A new particle representation scheme has been adopted for selecting the optimal number of clusters from several possible choices. The performance of the proposed method has been extensively compared with a few state of the art clustering techniques over a test suit of several artificial and real life datasets. Based on the computer simulations, some empirical guidelines have been provided for selecting the suitable parameters of the PSO algorithm.
A genetic algorithm approach for semisupervised clustering
 JOURNAL OF SMART ENGINEERING SYSTEM DESIGN
, 2002
"... A novel semisupervised clustering algorithm is proposed that synergizes the benefits of supervised and unsupervised learning methods. Data are clustered using an unsupervised learning technique biased toward producing clusters as pure as possible in terms of class distribution. These clusters can t ..."
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Cited by 5 (2 self)
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A novel semisupervised clustering algorithm is proposed that synergizes the benefits of supervised and unsupervised learning methods. Data are clustered using an unsupervised learning technique biased toward producing clusters as pure as possible in terms of class distribution. These clusters can then be used to predict the class of future points. For example in database marketing, this technique can be used to identify and characterize segments of the customer population likely to respond to a specific promotion. One key additional benefit of this approach is that it allows unlabeled data with unknown class to be used to improve classification accuracy. The objective function of a traditional clustering technique, cluster dispersion in the Kmeans algorithm, is modified to minimize both the within cluster variance of the input attributes and a measure of cluster impurity based on the class labels. Minimizing the within cluster variance of the examples is a form of capacity control to prevent overfitting. For the the output labels, impurity measures such as the Gini index can readily be applied to this problem. In this work, a genetic algorithm is proposed to optimize such an objective function to produce clusters. Nonempty clusters are labeled with the majority class. Experimental results show that using class information often improves the generalization ability compared to unsupervised methods based only on the input attributes. Benchmark studies also indicate that the method performs very well even when few training examples are available. Training using information from unlabeled data can improve classification accuracy on that data as well.
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
 IEEE Trans. Syst., Man, Cybern. B, Cybern
, 2005
"... Abstract—Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighte ..."
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Abstract—Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with themeans algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results. Index Terms—Cluster validity, clustering, evolutionary computation, genetic algorithms, niching methods. I.
Pairwise Nearest Neighbor Method Revisited
, 2004
"... The pairwise nearest neighbor (PNN) method, also known as Ward's method belongs to the class of agglomerative clustering methods. The PNN method generates hierarchical clustering using a sequence of merge operations until the desired number of clusters is obtained. This method selects the clust ..."
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Cited by 4 (0 self)
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The pairwise nearest neighbor (PNN) method, also known as Ward's method belongs to the class of agglomerative clustering methods. The PNN method generates hierarchical clustering using a sequence of merge operations until the desired number of clusters is obtained. This method selects the cluster pair to be merged so that it increases the given objective function value least. The main drawback of the PNN method is its slowness because the time complexity of the fastest known exact implementation of the PNN method is lower bounded by O(N²), where N is the number of data objects. We consider several speedup methods for the PNN method in the first publication. These methods maintain the precision of the method. Another method for speedingup the PNN method is investigated in the second publication, where we utilize a kneighborhood graph for reducing distance calculations and operations. A remarkable speedup is achieved at the cost of slight increase in distortion. The PNN method can also be adapted for multilevel thresholding, which can be seen as
Congressional districting using a TSPbased genetic algorithm
 of Lecture Notes in Computer Science
, 2003
"... Abstract. The drawing of congressional districts by legislative bodies in the United States creates a great deal of controversy each decade as political parties and special interest groups attempt to divide states into districts beneficial to their candidates. The genetic algorithm presented in this ..."
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Abstract. The drawing of congressional districts by legislative bodies in the United States creates a great deal of controversy each decade as political parties and special interest groups attempt to divide states into districts beneficial to their candidates. The genetic algorithm presented in this paper attempts to find a set of compact and contiguous congressional districts of approximately equal population. This genetic algorithm utilizes a technique based on an encoding and genetic operators used to solve Traveling Salesman Problems (TSP). This encoding forces near equality of district population and uses the fitness function to promote district contiguity and compactness. A postprocessing step further refines district population equality. Results are provided for three states (North Carolina, South Carolina, and Iowa) using 2000 census data. 1 Problem History The United States Congress consists of two houses, the Senate (containing two members from each of the fifty states) and the House of Representatives. The House of Representatives has 435 members, and each state is apportioned a congressional delegation in proportion to its population as determined by a national, decennial census. Each state (usually the state’s legislative body) is responsible for partitioning its state into a number of districts (a districting plan) equal to its apportionment. Through years of case law, the courts have outlined several requirements for the drawing of districts [1]. – The districts must be contiguous. – The districts must be of equal population following the “oneman onevote ” principle.