BioHEL is an evolutionary learning system designed to handle with large-scale bioinformatic datasets. BioHEL is strongly influenced by the GAssist Pittsburgh LCS, inheriting from it some main mechanisms. However, the main learning paradigm differs from the LCS standards to make this system more suitable for large scale domains. Moreover, a novel meta-representation called AKLR and a CUDA-based evaluation process are used to speed up the evaluation process, making possible for this system to solve very large and complex real life problems in less time.

References in zbMATH (referenced in 7 articles )

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  1. Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew Song, Una-May O’Reilly: Bring Your Own Learner: A Cloud-Based, Data-Parallel Commons for Machine Learning (2015) not zbMATH
  2. Franco, María A.; Krasnogor, Natalio; Bacardit, Jaume: GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning (2013) ioport
  3. Policicchio, Veronica L.; Pietramala, Adriana; Rullo, Pasquale: GAMoN: discovering (M)-of-(N^\neg, \lor) hypotheses for text classification by a lattice-based genetic algorithm (2012)
  4. Bacardit, Jaume; Stout, Michael; Hirst, Jonathan D.; Valencia, Alfonso; Smith, Robert Elliott; Krasnogor, Natalio: Automated alphabet reduction for protein datasets (2009) ioport
  5. Llorà, Larry Pier Luca Xavier; Priya, Anusha; Bhargava, Rohit: Observer-invariant histopathology using genetics-based machine learning (2009)
  6. Urbanowicz, Ryan J.; Moore, Jason H.: Learning classifier systems: a complete introduction, review, and roadmap (2009) ioport
  7. Bacardit, Jaume; Stout, Michael; Hirst, Jonathan D.; Krasnogor, Natalio: Data mining in proteomics with learning classifier systems (2008)