Incorporating Knowledge in Evolutionary Prototype Selection

  • Salvador García
  • José Ramón Cano
  • Francisco Herrera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Evolutionary algorithms has been recently used for prototype selection showing good results. An important problem in prototype selection consist in increasing the size of data sets. This problem can be harmful in evolutionary algorithms by deteriorating the convergence and increasing the time complexity. In this paper, we offer a preliminary proposal to solve these drawbacks. We propose an evolutionary algorithm that incorporates knowledge about the prototype selection problem. This study includes a comparison between our proposal and other evolutionary and non-evolutionary prototype selection algorithms. The results show that incorporating knowledge improves the performance of evolutionary algorithms and considerably reduces time execution.


Evolutionary Algorithm Partial Evaluation Premature Convergence Local Search Procedure Instance Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Salvador García
    • 1
  • José Ramón Cano
    • 2
  • Francisco Herrera
    • 1
  1. 1.Department of Computer Science and Artificial Intelligence, E.T.S.I. InformáticaUniversity of GranadaGranadaSpain
  2. 2.Department of Computer ScienceUniversity of JaénLinares, JaénSpain

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