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Evolving toxicity models using multigene symbolic regression and multiple objectives

Lookup NU author(s): Charles Hii, Dr Dominic Searson, Dr Mark Willis

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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.


Publication metadata

Author(s): Hii C, Searson DP, Willis MJ

Publication type: Article

Journal: International Journal of Machine Learning and Computing

Year: 2011

Volume: 1

Issue: 1

Pages: 30-35

Print publication date: 01/04/2011

ISSN (electronic): 2010-3700

Publisher: International Association of Computer Science and Information Technology Press (IACSIT)

URL: http://ijmlc.org/papers/05-L0037.pdf


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