Evolutionary Intelligence

, Volume 6, Issue 2, pp 57–72 | Cite as

Self organizing classifiers: first steps in structured evolutionary machine learning

  • Danilo Vasconcellos Vargas
  • Hirotaka Takano
  • Junichi Murata
Special Issue


Learning classifier systems (LCSs) are evolutionary machine learning algorithms, flexible enough to be applied to reinforcement, supervised and unsupervised learning problems with good performance. Recently, self organizing classifiers were proposed which are similar to LCSs but have the advantage that in its structured population no balance between niching and fitness pressure is necessary. However, more tests and analysis are required to verify its benefits. Here, a variation of the first algorithm is proposed which uses a parameterless self organizing map (SOM). This algorithm is applied in challenging problems such as big, noisy as well as dynamically changing continuous input-action mazes (growing and compressing mazes are included) with good performance. Moreover, a genetic operator is proposed which utilizes the topological information of the SOM’s population structure, improving the results. Thus, the first steps in structured evolutionary machine learning are shown, nonetheless, the problems faced are more difficult than the state-of-art continuous input-action multi-step ones.


Self organization Self organizing systems Self organizing map Learning classifier systems Reinforcement learning Structured evolutionary algorithms 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Danilo Vasconcellos Vargas
    • 1
  • Hirotaka Takano
    • 1
  • Junichi Murata
    • 1
  1. 1.Kyushu UniversityFukuokaJapan

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