Evolution of Multi-adaptive Discretization Intervals for a Rule-Based Genetic Learning System

  • Jaume Bacardit
  • Josep Maria Garrell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2527)


Genetic Based Machine Learning (GBML) systems traditionally have evolved rules that only deal with discrete attributes. Therefore, some discretization process is needed in order to teal with realvalued attributes.There are several methods to discretize real-valued attributes into a finite number of intervals, however none of them can efficiently solve all the possible problems.The alternative of a high number of simple uniform-width intervals usually expands the size of the search space without a clear performance gain.This paper proposes a rule representation which uses adaptive discrete intervals that split or merge through the evolution process, finding the correct discretization intervals at the same time as the learning process is done.


Search Space Conjunctive Normal Form Discretization Interval Rule Representation Attribute Term 
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 2002

Authors and Affiliations

  • Jaume Bacardit
    • 1
  • Josep Maria Garrell
    • 1
  1. 1.Intelligent Systems Research Group Enginyeria i Arquitectura La SalleUniversitat Ramon LlullSpainEurope

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