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Updated: Aug 13, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Genetic programming-based chaotic time series modeling
Wei Zhang1, Zhi-ming Wu, Gen-ke Yang
1Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China. zhang_wi@sjtu.edu.cn.
Abstract:
This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.
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