Paper Info
Title
Ecsago: Evolutionary Clustering With Self Adaptive Genetic Operators
Abstract
We present an algorithm for Evolutionary Clustering with Self Adaptive Genetic Operators (ECSAGO). This algorithm is based on the Unsupervised Niche Clustering (UNC) and Hybrid Adaptive Evolutionary (HAEA) algorithms. The UNC is a genetic clustering algorithm that is robust to noise and is able to determine the number of clusters automatically. HAEA is a parameter adaptation technique that automatically learns the rates of its genetic operators at the same time that the individuals are evolved in an Evolutionary Algorithm (EA). ECSAGO uses an EA with real encoding, real genetic operators, and adapts the genetic operator rates as it is evolving the cluster prototypes. This will have the advantage of reducing the number of parameters required by UNC (thus avoiding the problem of fixing the genetic operator parameter values), and solving problems where real representation is required or prefered for the solutions.
Year
DOI
Venue
2006
10.1109/CEC.2006.1688521
2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6
Keywords
Field
DocType
genetic operator,genetic algorithms,genetics,evolutionary algorithm
Canopy clustering algorithm,Genetic operator,Evolutionary algorithm,Correlation clustering,Genetic programming,Genetic representation,Artificial intelligence,Cultural algorithm,Evolutionary programming,Mathematics,Machine learning
Conference
Citations 
PageRank 
References 
3
0.40
11
Authors
3
Name
Order
Citations
PageRank
Elizabeth Leon1335.26
Olfa Nasraoui21515164.53
Jonatan Gomez3163.01