Evolutionary Modeling of Systems of Ordinary Differential Equations with Genetic Programming

  • Authors:

  • Hongqing Cao;Lishan Kang;Yuping Chen;Jingxian Yu

  • Affiliations:

  • State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, P. R. China/ State Key Laboratory of Parallel and Distributed Processing, P. R. China chq@rjgc.whu.edu.cnkang@whu.edu.cn;State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, P. R. China/ State Key Laboratory of Parallel and Distributed Processing, P. R. China;Institute of Electrochemistry, Department of Chemistry, Wuhan University, Wuhan 430072, P. R. China jxyu@263.net

  • Venue:
  • Genetic Programming and Evolvable Machines
  • Year:
  • 2000

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Abstract

This paper describes an approach to the evolutionary modeling problem of ordinary differential equations including systems of ordinary differential equations and higher-order differential equations. Hybrid evolutionary modeling algorithms are presented to implement the automatic modeling of one- and multi-dimensional dynamic systems respectively. The main idea of the method is to embed a genetic algorithm in genetic programming where the latter is employed to discover and optimize the structure of a model, while the former is employed to optimize its parameters. A number of practical examples are used to demonstrate the effectiveness of the approach. Experimental results show that the algorithm has some advantages over most available modeling methods.