Efficient Computation of Fitness Function for Evolutionary Clustering

Efficient Evolutionary Unsupervised Clustering

Evolutionary clustering is a new trend in cluster analysis, that has the potential to provide high partitioning accuracy results. Traditional evolutionary techniques applied in clustering are typically hindered by the high cost involved in the computation of the objective function. In this paper we propose a novel objective function, that is able to provide fitness function values in sub-linear...

A New Method for Geolocating of Radiation Sources Based on Evolutionary Computation of TDOA Equations

In this article a new method is introduced for geolocating of signal emitters which is based on evolutionary computation (EC) concept. In the proposed method two well-known members of EC techniques including Bees Algorithm (BA) and Genetic Algorithm (GA), are utilized to estimate the positions of emitters by optimizing the hyperbola equations which have been resulted from Time Difference of Arr...

Image Clustering Using Evolutionary Computation Techniques

In the cluster analysis most of the existing clustering techniques for clustering, accept the numbers of clusters K as an input and determine that many number of cluster for a given data set. The projecting technique will try to discover true number of cluster centers automatically on the run. It will not only determines the true number of the cluster centers but also extracts real cluster cent...

A comprehensive survey of fitness approximation in evolutionary computation

Evolutionary algorithms (EAs) have received increasing interests both in the academy and industry. One main difficulty in applying EAs to real-world applications is that EAs usually need a large number of fitness evaluations before a satisfying result can be obtained. However, fitness evaluations are not always straightforward in many real-world applications. Either an explicit fitness function...

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