Improved gene expression programming to solve the inverse problem for ordinary differential equations

Li-Shan, K., Chen, Y., Li, W., He, J. ORCID: 0000-0002-5616-4691 and Xue, Y., 2018. Improved gene expression programming to solve the inverse problem for ordinary differential equations. Swarm and Evolutionary Computation, 38, pp. 231-239. ISSN 2210-6502

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Abstract

Many complex systems in the real world evolve with time. These dynamic systems are often modeled by ordinary differential equations in mathematics. The inverse problem of ordinary differential equations is to convert the observed data of a physical system into a mathematical model in terms of ordinary differential equations. Then the modelay be used to predict the future behavior of the physical system being modeled. Genetic programming has been taken as a solver of this inverse problem. Similar to genetic programming, gene expression programming could do the same job since it has a similar ability of establishing the model of ordinary differential systems. Nevertheless, such research is seldom studied before. This paper is one of the first attempts to apply gene expression programming for solving the inverse problem of ordinary differential equations. Based on a statistic observation of traditional gene expression programming, an improvement is made in our algorithm, that is, genetic operators should act more often on the dominant part of genes than on the recessive part. This may help maintain population diversity and also speed up the convergence of the algorithm. Experiments show that this improved algorithm performs much better than genetic programming and traditional gene expression programming in terms of running time and prediction precision

Item Type: Journal article
Publication Title: Swarm and Evolutionary Computation
Creators: Li-Shan, K., Chen, Y., Li, W., He, J. and Xue, Y.
Publisher: Elsevier
Date: February 2018
Volume: 38
ISSN: 2210-6502
Identifiers:
NumberType
10.1016/j.swevo.2017.07.005DOI
S2210650216305910Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 28 Mar 2018 15:05
Last Modified: 03 May 2018 13:31
URI: http://irep.ntu.ac.uk/id/eprint/33142

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