Memetic Computing

, Volume 1, Issue 1, pp 55–67 | Cite as

Improving the scalability of rule-based evolutionary learning

  • Jaume Bacardit
  • Edmund K. Burke
  • Natalio KrasnogorEmail author
Regular Research Paper


Evolutionary learning techniques are comparable in accuracy with other learning methods such as Bayesian Learning, SVM, etc. These techniques often produce more interpretable knowledge than, e.g. SVM; however, efficiency is a significant drawback. This paper presents a new representation motivated by our observations that Bioinformatics and Systems Biology often give rise to very large-scale datasets that are noisy, ambiguous and usually described by a large number of attributes. The crucial observation is that, in the most successful rules obtained for such datasets, only a few key attributes (from the large number of available ones) are expressed in a rule, hence automatically discovering these few key attributes and only keeping track of them contributes to a substantial speed up by avoiding useless match operations with irrelevant attributes. Thus, in effective terms this procedure is performing a fine-grained feature selection at a rule-wise level, as the key attributes may be different for each learned rule. The representation we propose has been tested within the BioHEL machine learning system, and the experiments performed show that not only the representation has competent learning performance, but that it also manages to reduce considerably the system run-time. That is, the proposed representation is up to 2–3 times faster than state-of-the-art evolutionary learning representations designed specifically for efficiency purposes.


Evolutionary algorithms Evolutionary learning Learning classifier systems Rule induction Bioinformatics Protein structure prediction 


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Jaume Bacardit
    • 1
    • 2
  • Edmund K. Burke
    • 1
  • Natalio Krasnogor
    • 1
    Email author
  1. 1.ASAP Research Group, School of Computer ScienceUniversity of NottinghamNottinghamUK
  2. 2.Multidisciplinary Centre for Integrative Biology, School of BiosciencesUniversity of NottinghamSutton BoningtonUK

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