Evolving Additive Trees for Modeling Biochemical Systems

This paper presents a hybrid evolutionary method for identifying a system of ordinary differential equations (ODEs) from the observed time series. In this approach, the tree-structure based evolution algorithm and particle swarm optimization (PSO) are employed to evolve the architecture and the parameters of the additive tree models for the problem of system identification. Experimental results on modeling biochemical system show that the proposed method is more feasible and effective than other related works.

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