A Multi-Objective Evolutionary Algorithm Fitness Function for Case-Base Maintenance

  • Eduardo Lupiani
  • Susan Craw
  • Stewart Massie
  • Jose M. Juarez
  • Jose T. Palma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7969)


Case-Base Maintenance (CBM) has two important goals. On the one hand, it aims to reduce the size of the case-base. On the other hand, it has to improve the accuracy of the CBR system. CBM can be represented as a multi-objective optimization problem to achieve both goals. Multi-Objective Evolutionary Algorithms (MOEAs) have been recognised as appropriate techniques for multi-objective optimisation because they perform a search for multiple solutions in parallel. In the present paper we introduce a fitness function based on the Complexity Profiling model to perform CBM with MOEA, and we compare its results against other known CBM approaches. From the experimental results, CBM with MOEA shows regularly good results in many case-bases, despite the amount of redundant and noisy cases, and with a significant potential for improvement.


Multiobjective Evolutionary Algorithm Noisy Case Binary Tournament Selection Minimum Error Rate Noisy Dataset 
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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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Eduardo Lupiani
    • 1
  • Susan Craw
    • 2
  • Stewart Massie
    • 2
  • Jose M. Juarez
    • 1
  • Jose T. Palma
    • 1
  1. 1.University of MurciaSpain
  2. 2.The Robert Gordon UniversityScotland, UK

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