符号回归
遗传程序设计
工具箱
计算机科学
文档
MATLAB语言
数据挖掘
机器学习
回归分析
数量结构-活动关系
回归
人工智能
程序设计语言
统计
数学
作者
Dominic P. Searson,David E. Leahy,M.J. Willis
出处
期刊:International MultiConference of Engineers and Computer Scientists
日期:2010-01-01
被引量:45
摘要
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 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/.
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