Journal of Intelligent Information Systems

, Volume 46, Issue 2, pp 259–284 | Cite as

Case-base maintenance with multi-objective evolutionary algorithms

  • Eduardo LupianiEmail author
  • Stewart Massie
  • Susan Craw
  • Jose M. Juarez
  • Jose Palma


Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solving ability of the Case-Base Reasoning system. However, when the case-base has many cases, then performance problems arise due to the time needed to find those similar cases to the input problem. At this point, Case-Base Maintenance algorithms can be used to reduce the number of cases and maintain the accuracy of the Case-Base Reasoning system at the same time. Whereas Case-Base Maintenance algorithms typically use a particular heuristic to remove (or select) cases from the case-base, the resulting maintained case-base relies on the proportion of redundant and noisy cases that are present in the case-base, among other factors. That is, a particular Case-Base Maintenance algorithm is suitable for certain types of case-bases that share some indicators, such as redundancy and noise levels. In the present work, we consider Case-Base Maintenance as a multi-objective optimization problem, which is solved with a Multi-Objective Evolutionary Algorithm. To this end, a fitness function is introduced to measure three different objectives based on the Complexity Profile model. Our hypothesis is that the Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance may be used in a wider set of case-bases, achieving a good balance between the reduction of cases and the problem-solving ability of the Case-Based Reasoning system. Finally, from a set of the experiments, our proposed Multi-Objective Evolutionary Algorithm performing Case-Base Maintenance shows regularly good results with different sets of case-bases with different proportion of redundant and noisy cases.


Case-based reasoning Case-base maintenance Multi-objective evolutionary algorithms 



This work was partially funded by the Seneca Research Foundation of the Region of Murcia under project 15277/PI/10, and by the Spanish Ministry of Science and Innovation+European FEDER+PlanE funds under the project TIN2009-14372-C03-01.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Eduardo Lupiani
    • 1
    Email author
  • Stewart Massie
    • 2
  • Susan Craw
    • 2
  • Jose M. Juarez
    • 1
  • Jose Palma
    • 1
  1. 1.University of MurciaMurciaSpain
  2. 2.Robert Gordon UniversityAberdeenUK

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