doi: 10.7763/IJMLC.2011.V1.5
Evolving Toxicity Models using Multigene Symbolic Regression and Multiple Objectives
,
Dominic P. Searson
,
Mark J. Willis
- School of Chemical Engineering and Advanced Materials at the University of Newcastle, Newcastle-upon-Tyne, UK.
Abstract
In this contribution a multi-objective genetic programming algorithm (MOGP) is used to perform symbolic regression. The genetic programming (GP) algorithm used 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 MOGP algorithm simultaneously optimizes the dual (and competing) objectives of maximization of ‘goodness-of-fit’ to data and minimization of model complexity in order to develop parsimonious data based symbolic models. The functionality of the multigene MOGP algorithm is demonstrated by using it to generate an accurate,compact QSAR (quantitative structure activity relationship)model of existing toxicity data in order to predict the toxicity of chemical compounds.
Keywords
- genetic programming
- multi-objective optimization
- symbolic regression
- QSAR
- toxicity
- T. pyriformis
How to Cite
Charles Hii, Dominic P. Searson, and Mark J. Willis, "Evolving Toxicity Models using Multigene Symbolic Regression and Multiple Objectives," International Journal of Machine Learning, vol. 1, no. 1, pp. 30-35, 2011. https://doi.org/10.7763/IJMLC.2011.V1.5
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).