Boosting CBR Agents with Genetic Algorithms

  • Beatriz López
  • Carles Pous
  • Albert Pla
  • Pablo Gay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5650)


In this paper we present a distributed system in which several case-based reasoning (CBR) agents cooperate under a boosting schema. Each CBR agent knows part of the cases (a subset of the available attributes) and is trained with a subset of the available cases (so not all the agents know the same cases). The solution of the system is then computed by means of a weighted average of the solutions provided by the CBR agents. Weights are actively learnt by a genetic algorithm. The system has been applied to a breast cancer application domain. The results show that with our methodology we can improve the results obtained with a case base in which attributes have been manually selected by physicians, saving physicians work in future.


Distributed CBR genetic algorithms boosting multi-agent systems 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Beatriz López
    • 1
  • Carles Pous
    • 1
  • Albert Pla
    • 1
  • Pablo Gay
    • 1
  1. 1.University of GironaGironaSpain

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