A Re-Examination of the Use of Genetic Programming on the Oral Bioavailability Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Grant Dick and Aysha P. Rimoni and Peter A. Whigham",
  title =        "A Re-Examination of the Use of Genetic Programming on
                 the Oral Bioavailability Problem",
  booktitle =    "GECCO '15: Proceedings of the 2015 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2015",
  editor =       "Sara Silva and Anna I Esparcia-Alcazar and 
                 Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and 
                 Christine Zarges and Luis Correia and Terence Soule and 
                 Mario Giacobini and Ryan Urbanowicz and 
                 Youhei Akimoto and Tobias Glasmachers and 
                 Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and 
                 Marta Soto and Carlos Cotta and Francisco B. Pereira and 
                 Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and 
                 Heike Trautmann and Jean-Baptiste Mouret and 
                 Sebastian Risi and Ernesto Costa and Oliver Schuetze and 
                 Krzysztof Krawiec and Alberto Moraglio and 
                 Julian F. Miller and Pawel Widera and Stefano Cagnoni and 
                 JJ Merelo and Emma Hart and Leonardo Trujillo and 
                 Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and 
                 Carola Doerr",
  isbn13 =       "978-1-4503-3472-3",
  pages =        "1015--1022",
  keywords =     "genetic algorithms, genetic programming",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739480.2754771",
  DOI =          "doi:10.1145/2739480.2754771",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Difficult benchmark problems are in increasing demand
                 in Genetic Programming (GP). One problem seeing
                 increased usage is the oral bioavailability problem,
                 which is often presented as a challenging problem to
                 both GP and other machine learning methods. However,
                 few properties of the bioavailability data set have
                 been demonstrated, so attributes that make it a
                 challenging problem are largely unknown. This work
                 uncovers important properties of the bioavailability
                 data set, and suggests that the perceived difficulty in
                 this problem can be partially attributed to a lack of
                 pre-processing, including features within the data set
                 that contain no information, and contradictory
                 relationships between the dependent and independent
                 features of the data set. The paper then re-examines
                 the performance of GP on this data set, and
                 contextualises this performance relative to other
                 regression methods. Results suggest that a large
                 component of the observed performance differences on
                 the bioavailability data set can be attributed to
                 variance in the selection of training and testing data.
                 Differences in performance between GP and other methods
                 disappear when multiple training/testing splits are
                 used within experimental work, with performance
                 typically no better than a null modelling approach of
                 reporting the mean of the training data.",
  notes =        "Also known as \cite{2754771} GECCO-2015 A joint
                 meeting of the twenty fourth international conference
                 on genetic algorithms (ICGA-2015) and the twentith
                 annual genetic programming conference (GP-2015)",

Genetic Programming entries for Grant Dick Aysha Rimoni Peter Alexander Whigham