Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming

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@InProceedings{DBLP:conf/epia/VanneschiS09,
  author =       "Leonardo Vanneschi and Sara Silva",
  title =        "Using Operator Equalisation for Prediction of Drug
                 Toxicity with Genetic Programming",
  booktitle =    "Progress in Artificial Intelligence, 14th Portuguese
                 Conference on Artificial Intelligence, EPIA 2009",
  year =         "2009",
  editor =       "Luis Seabra Lopes and Nuno Lau and Pedro Mariano and 
                 Luis Mateus Rocha",
  volume =       "5816",
  series =       "LNAI",
  pages =        "65--76",
  address =      "Aveiro, Portugal",
  month =        oct # " 12-15",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-04685-8",
  DOI =          "doi:10.1007/978-3-642-04686-5_6",
  abstract =     "Predicting the toxicity of new potential drugs is a
                 fundamental step in the drug design process. Recent
                 contributions have shown that, even though Genetic
                 Programming is a promising method for this task, the
                 problem of predicting the toxicity of molecular
                 compounds is complex and difficult to solve. In
                 particular, when executed for predicting drug toxicity,
                 Genetic Programming undergoes the well-known phenomenon
                 of bloat, i.e. the growth in code size during the
                 evolutionary process without a corresponding
                 improvement in fitness. We hypothesize that this might
                 cause overfitting and thus prevent the method from
                 discovering simpler and potentially more general
                 solutions. For this reason, in this paper we
                 investigate two recently defined variants of the
                 operator equalization bloat control method for Genetic
                 Programming. We show that these two methods are bloat
                 free also when executed on this complex problem.
                 Nevertheless, overfitting still remains an issue. Thus,
                 contradicting the generalized idea that bloat and
                 overfitting are strongly related, we argue that the two
                 phenomena are independent from each other and that
                 eliminating bloat does not necessarily eliminate
                 overfitting.",
  notes =        "EPIA http://dx.doi.org/10.1007/978-3-642-04686-5",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
}

Genetic Programming entries for Leonardo Vanneschi Sara Silva

Citations