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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| 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 |
| ISSN (electronic) | | 2010-3700 |
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| Full text for this publication is not currently held within this repository. Alternative links are provided below where available. |
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| 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. |
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| Publisher | | International Association of Computer Science and Information Technology Press (IACSIT) |
| URL | | http://ijmlc.org/papers/05-L0037.pdf |
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