Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization. Usman, M., Awad, A., Pang, W., & Coghill, G. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 101–102, United States, July, 2019. Association for Computing Machinery.
doi  abstract   bibtex   
Inferring models of dynamic systems from their time series data is a challenging task for optimization algorithms due to its potentially expensive computational cost and underlying large search space. In this study, we aim to infer both the structure and parameters of a dynamic system model simultaneously by Particle Swarm Optimization (PSO), enhanced by effective stratified sampling strategies.More specifically, we apply Latin Hyper Cube Sampling (LHS) with PSO. This leads to two novel swarm-inspired algorithms, LHS-PSO which can be used efficiently to learn the structure and parameters of simple and complex dynamic system models. We used a complex biological cancer model called Kinetochores, for assessing the performance of PSO and LHS-PSO. The experimental resultsdemonstrate that LHS-PSO can find promising solutions with corresponding structure and parameters, and it outperforms PSO during our experiments.
@inproceedings{e46fae6a13634205ad9e6ae71deb1700,  title     = "Inferring Structure and Parameters of Dynamic Systems using Latin Hypercube Sampling Multi Dimensional Uniformity-Particle Swarm Optimization",  abstract  = "Inferring models of dynamic systems from their time series data is a challenging task for optimization algorithms due to its potentially expensive computational cost and underlying large search space. In this study, we aim to infer both the structure and parameters of a dynamic system model simultaneously by Particle Swarm Optimization (PSO), enhanced by effective stratified sampling strategies.More specifically, we apply Latin Hyper Cube Sampling (LHS) with PSO. This leads to two novel swarm-inspired algorithms, LHS-PSO which can be used efficiently to learn the structure and parameters of simple and complex dynamic system models. We used a complex biological cancer model called Kinetochores, for assessing the performance of PSO and LHS-PSO. The experimental resultsdemonstrate that LHS-PSO can find promising solutions with corresponding structure and parameters, and it outperforms PSO during our experiments.",  keywords  = "Dynamic Systems, Particle Swarm Optimization, Genetic Algorithm, Latin Hypercube Sampling, Learning Structure and Parameter, Parameter, Learning Structure",  author    = "Muhammad Usman and Abubakr Awad and Wei Pang and Coghill, {George M.}",  year      = "2019",  month     = jul,  day       = "13",  doi       = "10.1145/3319619.3322010",  language  = "English",  isbn      = "9781450367486",  pages     = "101--102",  booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion",  publisher = "Association for Computing Machinery",  address   = "United States", }
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