TY  - JOUR
AU  - Gálvez, Jorge
AU  - Hernández, Gustavo R.
AU  - Avalos, Omar
AU  - Casas-Ordaz, Angel
AU  - Perez-Cinseros, Marco
AU  - Oliva, Diego
PY  - 2025
DA  - 2025/12/20
TI  - A Comparison of Evolutionary Computation Techniques for Parameter Estimation of Chaotic Systems
JO  - ICCK Transactions on Swarm and Evolutionary Learning
T2  - ICCK Transactions on Swarm and Evolutionary Learning
JF  - ICCK Transactions on Swarm and Evolutionary Learning
VL  - 1
IS  - 2
SP  - 83
EP  - 93
DO  - 10.62762/TSEL.2025.913117
UR  - https://www.icck.org/article/abs/TSEL.2025.913117
KW  - evolutionary computation
KW  - chaotic systems
KW  - lorenz system
KW  - chen system
AB  - In recent years, Parameter Estimation (PE) has become a topic of growing interest due to its broad applications in science and engineering. An important application is the identification of Chaotic Systems (CS), which enables synchronization and control of chaotic behavior. However, the parameter estimation of CS is a highly nonlinear and multidimensional optimization problem where traditional approaches are often unsuitable. To overcome these limitations, Evolutionary Computation Techniques (ECT) have been widely adopted to tackle complex nonlinear optimization tasks. Recently, classical and modern ECT methods have been proposed for estimating the parameters of chaotic systems. However, most reported studies rely exclusively on cost function values, overlooking the quality and consistency of the solutions obtained. This paper presents a comparative study of representative evolutionary techniques for estimating the parameters of chaotic systems. The study assesses the performance of the techniques and the homogeneity of the solutions through statistical analysis. Experimental results on the Lorenz and Chen systems are examined and validated using nonparametric tests.
SN  - 3069-2962
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Glvez2025A,
  author = {Jorge Gálvez and Gustavo R. Hernández and Omar Avalos and Angel Casas-Ordaz and Marco Perez-Cinseros and Diego Oliva},
  title = {A Comparison of Evolutionary Computation Techniques for Parameter Estimation of Chaotic Systems},
  journal = {ICCK Transactions on Swarm and Evolutionary Learning},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {83-93},
  doi = {10.62762/TSEL.2025.913117},
  url = {https://www.icck.org/article/abs/TSEL.2025.913117},
  abstract = {In recent years, Parameter Estimation (PE) has become a topic of growing interest due to its broad applications in science and engineering. An important application is the identification of Chaotic Systems (CS), which enables synchronization and control of chaotic behavior. However, the parameter estimation of CS is a highly nonlinear and multidimensional optimization problem where traditional approaches are often unsuitable. To overcome these limitations, Evolutionary Computation Techniques (ECT) have been widely adopted to tackle complex nonlinear optimization tasks. Recently, classical and modern ECT methods have been proposed for estimating the parameters of chaotic systems. However, most reported studies rely exclusively on cost function values, overlooking the quality and consistency of the solutions obtained. This paper presents a comparative study of representative evolutionary techniques for estimating the parameters of chaotic systems. The study assesses the performance of the techniques and the homogeneity of the solutions through statistical analysis. Experimental results on the Lorenz and Chen systems are examined and validated using nonparametric tests.},
  keywords = {evolutionary computation, chaotic systems, lorenz system, chen system},
  issn = {3069-2962},
  publisher = {Institute of Central Computation and Knowledge}
}