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A multi-objective evolutionary algorithm fitness function for case-base maintenance.

Lupiani, Eduardo; Craw, Susan; Massie, Stewart; Juarez, Jose M.; Palma, Jose T.


Eduardo Lupiani

Jose M. Juarez

Jose T. Palma


Sarah Jane Delany

Santiago Onta��n


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.


LUPIANI, E., CRAW, S., MASSIE, S., JUAREZ, J.M. and PALMA, J.T. 2013. A multi-objective evolutionary algorithm fitness function for case-base maintenance. In Delany, S.J. and Ontañón, S. (eds.) Case-based reasoning research and development: proceedings of the 21st International conference on case-based reasoning (ICCBR 2013), 8-11 July 2013, Saratoga Springs, USA. Lecture notes in computer science, 7969. Berlin: Springer [online], pages 218-232. Available from:

Conference Name 21st International conference on case-based reasoning (ICCBR 2013)
Conference Location Saratoga Springs, USA
Start Date Jul 8, 2013
End Date Jul 11, 2013
Acceptance Date Jul 31, 2013
Online Publication Date Jul 31, 2013
Publication Date Dec 31, 2013
Deposit Date Oct 22, 2015
Publicly Available Date Oct 22, 2015
Publisher Springer
Pages 218-232
Series Title Lecture notes in computer science
Series Number 7969
ISBN 9783642390555
Keywords Multiobjective Evolutionary Algorithm; Noisy Case; Binary Tournament Selection; Minimum Error Rate; Noisy Dataset
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