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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Dick:2015:GECCO,

• 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

Citations