GPTIPS: An Open Source Genetic Programming Toolbox For Multigene Symbolic Regression

  1. Lookup NU author(s)
  2. Dr Dominic Searson
  3. Professor David Leahy
  4. Dr Mark Willis
Author(s)Searson DP, Leahy DE, Willis MJ
Editor(s)Ao, S.I., Castillo, O., Douglas, C., Feng, D.D., Lee, J.-A.
Publication type Conference Proceedings (inc. Abstract)
Conference NameInternational MultiConference of Engineers and Computer Scientists 2010 (IMECS)
Conference LocationKowloon, Hong Kong
Year of Conference2010
Legacy Date7-19 March 2010
Volume1
Number of Volumes3
Pages77-80
Series TitleLecture Notes in Engineering and Computer Science
ISBN9789881701282
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
In this contribution GPTIPS, a free, open source MATLAB toolbox for performing symbolic regression by genetic programming (GP) is introduced. GPTIPS is specifically designed to evolve mathematical models of predictor response data that are 'multigene' in nature, i.e. linear combinations of low order non-linear transformations of the input variables. The functionality of GPTIPS is demonstrated by using it to generate an accurate, compact QSAR (quantitative structure activity relationship) model of an existing toxicity data in order to predict the toxicity of chemical compounds. It is shown that the low-order multigene GP methods implemented by GPTIPS can provide a useful alternative, as well as a complementary approach, to currently accepted empirical modelling and data analysis techniques.GPTIPS and documentation is available for download at http://sites.google.com/site/gptips4matlab/.
PublisherNewswood Ltd.
URLhttp://www.iaeng.org/publication/IMECS2010/IMECS2010_pp77-80.pdf
ActionsLink to this publication
Library holdingsSearch Newcastle University Library for this item