Bioinformatics-oriented Hierarchical Evolutionary Learning

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.

Documentation

To learn how to compile, run and configure the BioHEL system read the tutorial. In addition to the installation basics it explains the advanced configuration of the system features useful in tweaking the data mining process towards better or more adequate results.

To improve the generality of the final BioHEL solutions you can use the Rule Post-processing Engine.

Download

You can download the BioHEL C++ source code and compile it either for serial execution (CPU mode) or the parallel execution on GPU (up to 60x speedup, requires CUDA enabled graphics card to run). See the tutorial for details.

Project team

• Maria Franco
• Jaume Bacardit
• Natalio Krasnogor

Publications

If you use BioHEL, please cite Bacardit2009 (serial version) and Franco2010 (parallel version).

We would also be happy to list your publications on BioHEL on this website, so feel free to contact us.

• DOI BibTeX
  M.A. Franco, N. Krasnogor, J. Bacardit
  GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning
  in Soft Computing, 17(6):953-981, June 2013

  @ARTICLE{Franco2013,
    title = {GAssist vs. BioHEL: critical assessment of two paradigms of genetics-based machine learning},
    author = {Franco, María A. and Krasnogor, Natalio and Bacardit, Jaume},
    year = 2013,
    doi = {10.1007/s00500-013-1016-8},
    month = jun,
    journal = {Soft Computing},
    volume = {17},
    number = {6},
    pages = {953--981}
  }
  

• DOI slides code BibTeX
  M.A. Franco, N. Krasnogor, J. Bacardit
  Post-processing operators for decision lists
  in Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12, p.847, Philadelphia, Pennsylvania, USA, 2012

  @INPROCEEDINGS{Franco2012a,
    title = {Post-processing operators for decision lists},
    author = {Franco, Maria A. and Krasnogor, Natalio and Bacardit, Jaume},
    year = 2012,
    doi = {10.1145/2330163.2330281},
    booktitle = {Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO '12},
    pages = {847},
    address = {Philadelphia, Pennsylvania, USA}
  }
  

• DOI slides data BibTeX
  M.A. Franco, N. Krasnogor, J. Bacardit
  Analysing BioHEL using challenging boolean functions
  in Evolutionary Intelligence, 5(2):87-102, June 2012

  @ARTICLE{Franco2012,
    title = {Analysing BioHEL using challenging boolean functions},
    author = {Franco, María A. and Krasnogor, Natalio and Bacardit, Jaume},
    year = 2012,
    doi = {10.1007/s12065-012-0080-9},
    month = jun,
    journal = {Evolutionary Intelligence},
    volume = {5},
    number = {2},
    pages = {87--102}
  }
  

• DOI slides BibTeX
  M.A. Franco, N. Krasnogor, J. Bacardit
  Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding
  in Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11, p.1291, Dublin, Ireland, 2011
  Best Paper Award in GBML track

  @INPROCEEDINGS{Franco2011,
    title = {Modelling the initialisation stage of the ALKR representation for discrete domains and GABIL encoding},
    author = {Franco, Maria A. and Krasnogor, Natalio and Bacardit, Jaume},
    year = 2011,
    doi = {10.1145/2001576.2001750},
    note = {Best Paper Award in GBML track},
    booktitle = {Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11},
    pages = {1291},
    address = {Dublin, Ireland}
  }
  

• DOI BibTeX
  G.W. Bassel, E. Glaab, J. Marquez, M.J. Holdsworth, J. Bacardit
  Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets
  in THE PLANT CELL ONLINE, 23(9):3101-3116, September 2011

  @ARTICLE{Bassel2011,
    title = {Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets},
    author = {Bassel, G. W. and Glaab, E. and Marquez, J. and Holdsworth, M. J. and Bacardit, J.},
    year = 2011,
    doi = {10.1105/tpc.111.088153},
    month = sep,
    journal = {THE PLANT CELL ONLINE},
    volume = {23},
    number = {9},
    pages = {3101--3116}
  }
  

• DOI eprint slides BibTeX
  M.A. Franco, N. Krasnogor, J. Bacardit
  Speeding up the evaluation of evolutionary learning systems using GPGPUs
  in Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO '10, p.1039, Portland, Oregon, USA, 2010
  Best Paper Award in the GBML track

  @INPROCEEDINGS{Franco2010,
    title = {Speeding up the evaluation of evolutionary learning systems using GPGPUs},
    author = {Franco, María A. and Krasnogor, Natalio and Bacardit, Jaume},
    year = 2010,
    doi = {10.1145/1830483.1830672},
    note = {Best Paper Award in the GBML track},
    booktitle = {Proceedings of the 12th annual conference on Genetic and evolutionary computation - GECCO '10},
    pages = {1039},
    address = {Portland, Oregon, USA}
  }
  

• DOI slides BibTeX
  M.A. Franco, N. Krasnogor, J. Bacardit
  Analysing bioHEL using challenging boolean functions
  in Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO '10, p.1855, Portland, Oregon, USA, 2010

  @INPROCEEDINGS{Franco2010a,
    title = {Analysing bioHEL using challenging boolean functions},
    author = {Franco, Maria A. and Krasnogor, Natalio and Bacardit, Jaume},
    year = 2010,
    doi = {10.1145/1830761.1830817},
    booktitle = {Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO '10},
    pages = {1855},
    address = {Portland, Oregon, USA}
  }
  

• DOI eprint BibTeX
  J. Bacardit, E.K. Burke, N. Krasnogor
  Improving the scalability of rule-based evolutionary learning
  in Memetic Computing, 1(1):55-67, March 2009

  @ARTICLE{Bacardit2009,
    title = {Improving the scalability of rule-based evolutionary learning},
    author = {Bacardit, Jaume and Burke, Edmund K. and Krasnogor, Natalio},
    year = 2009,
    doi = {10.1007/s12293-008-0005-4},
    month = mar,
    journal = {Memetic Computing},
    volume = {1},
    number = {1},
    pages = {55--67}
  }
  

• DOI eprint BibTeX
  J. Bacardit, M. Stout, J.D. Hirst, A. Valencia, R.E. Smith, N. Krasnogor
  Automated Alphabet Reduction for Protein Datasets
  in BMC Bioinformatics, 10(1):6, 2009

  @ARTICLE{Bacardit2009a,
    title = {Automated Alphabet Reduction for Protein Datasets},
    author = {Bacardit, Jaume and Stout, Michael and Hirst, Jonathan D and Valencia, Alfonso and Smith, Robert E and Krasnogor, Natalio},
    year = 2009,
    doi = {10.1186/1471-2105-10-6},
    journal = {BMC Bioinformatics},
    volume = {10},
    number = {1},
    pages = {6}
  }
  

• DOI eprint BibTeX
  J. Bacardit, N. Krasnogor
  A mixed discrete-continuous attribute list representation for large scale classification domains
  in Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09, p.1155, Montreal, Québec, Canada, 2009

  @INPROCEEDINGS{Bacardit2009b,
    title = {A mixed discrete-continuous attribute list representation for large scale classification domains},
    author = {Bacardit, Jaume and Krasnogor, Natalio},
    year = 2009,
    doi = {10.1145/1569901.1570057},
    booktitle = {Proceedings of the 11th Annual conference on Genetic and evolutionary computation - GECCO '09},
    pages = {1155},
    address = {Montreal, Québec, Canada}
  }
  

• DOI BibTeX
  J. Bacardit, N. Krasnogor
  Fast rule representation for continuous attributes in genetics-based machine learning
  in Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08, p.1421, Atlanta, GA, USA, 2008

  @INPROCEEDINGS{Bacardit2008,
    title = {Fast rule representation for continuous attributes in genetics-based machine learning},
    author = {Bacardit, Jaume and Krasnogor, Natalio},
    year = 2008,
    doi = {10.1145/1389095.1389369},
    booktitle = {Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08},
    pages = {1421},
    address = {Atlanta, GA, USA}
  }
  

• DOI BibTeX
  M. Stout, J. Bacardit, J.D. Hirst, N. Krasnogor
  Prediction of recursive convex hull class assignments for protein residues
  in Bioinformatics, 24(7):916-923, April 2008

  @ARTICLE{Stout2008,
    title = {Prediction of recursive convex hull class assignments for protein residues},
    author = {Stout, M. and Bacardit, J. and Hirst, J. D. and Krasnogor, N.},
    year = 2008,
    doi = {10.1093/bioinformatics/btn050},
    month = apr,
    journal = {Bioinformatics},
    volume = {24},
    number = {7},
    pages = {916--923}
  }