Improving the scalability of rule-based evolutionary learning

@article{Bacardit2009ImprovingTS,
  title={Improving the scalability of rule-based evolutionary learning},
  author={Jaume Bacardit and Edmund K. Burke and Natalio Krasnogor},
  journal={Memetic Computing},
  year={2009},
  volume={1},
  pages={55-67}
}
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… 
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