Journal of System Simulation
Abstract
Abstract: To address the issues of complex node relationships and low accuracy in large-scale Boolean network inference, a new optimization algorithm integrated with long short-term memory (LSTM) networks and genetic programming was proposed. An enhanced LSTM network combined with a self-attention mechanism was designed to extract potential regulatory nodes from time-series data. These nodes were utilized as terminals of the syntax tree for the design of the genetic programming algorithm, and new operators were introduced to optimize Boolean function search. Experimental results have demonstrated that the proposed method significantly outperforms the most advanced existing algorithms in inference accuracy. The Boolean network optimization algorithm provides an effective tool for modeling and simulating dynamic behaviors in complex networks.
Recommended Citation
Tang, Jinlin; Wang, Yan; Liu, Xiang; Wang, Tuanjie; and Ji, Zhicheng
(2025)
"Boolean Network Model Optimization Based on Neural Network and Genetic Programming,"
Journal of System Simulation: Vol. 37:
Iss.
11, Article 10.
DOI: 10.16182/j.issn1004731x.joss.25-0334
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss11/10
First Page
2812
Last Page
2825
CLC
TP301.6
Recommended Citation
Tang Jinlin, Wang Yan, Liu Xiang, et al. Boolean Network Model Optimization Based on Neural Network and Genetic Programming[J]. Journal of System Simulation, 2025, 37(11): 2812-2825.
DOI
10.16182/j.issn1004731x.joss.25-0334
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons