ECSAGO: Evolutionary Clustering with Self Adaptive Genetic Operators

  • Leon E
  • Nasraoui O
  • Gomez J
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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.

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Authors

  • E. Leon

  • O. Nasraoui

  • J. Gomez

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