文章基本信息

标题：Inference of Differential Equations by Using Genetic Programming
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作者：Naoya SUGIMOTO ; Erina SAKAMOTO ; Hitoshi IBA 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2004
卷号：19
期号：6
页码：450-459
DOI：10.1527/tjsai.19.450
出版社：The Japanese Society for Artificial Intelligence
摘要：The ordinary differential equations (ODEs) are used as a mathematical method for the sake of modeling a complicated nonlinear system. This approach is well-known to be useful for the practical application, e.g., bioinformatics, chemical reaction models, controlling theory etc. In this paper, we propose a new evolutionary method by which to make inference of a system of ODEs. To explore the search space more effectively in the course of evolution, the right-hand sides of ODEs are inferred by Genetic Programming (GP) and the least mean square (LMS) method is used along with the ordinary GP. We apply our method to several target tasks and empirically show how successfully GP infers the systems of ODEs. We also describe how our approach is extended to solve the inference of a differential equation system including transdential functions.
关键词：genetic programming ; differential equations ; least mean square ; reverse engineering