Effective Training of PINNs by Combining CMA-ES with Gradient Descent

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{liu:2024:CEC,

• author = "Lin Liu and Yuan Yuan",
• title = "Effective Training of {PINNs} by Combining {CMA-ES} with Gradient Descent",
• booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2024",
• editor = "Bing Xue",
• address = "Yokohama, Japan",
• month = "30 " # jun # " - 5 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Training, Memetics, Neural networks, Evolutionary computation, Pareto optimization, Prediction algorithms, Linear programming, Physics-informed neural networks, evolutionary algorithm, gradient descent, memetic algorithm, multi-objective optimization",
• isbn13 = "979-8-3503-0837-2",
• DOI = "doi:10.1109/CEC60901.2024.10611964",
• abstract = "Physics-Informed Neural Networks (PINNs) have recently received increasing attention, however, optimising the loss function of PINNs is notoriously difficult, where the landscape of the loss function is often highly non-convex and rugged. Local optimisation methods based on gradient information can converge quickly but are prone to being trapped in local minima for training PINNs. Evolutionary algorithms (EAs) are well known for the global search ability, which can help escape from local minima. It has been reported in the literature that EAs show some advantages over gradient-based methods in training PINNs. Inspired by the Memetic Algorithm, we combine global-search based EAs and local-search based batch gradient descent in order to make the best of both word. In addition, since the PINN loss function is composed of multiple terms, balancing these terms is also a challenging issue. Therefore, we also attempt to combine EAs with multiple-gradient descent algorithm for multi-objective optimisation. Our experiments provide strong evidence for the superiority of the above algorithms.",
• notes = "also known as \cite{10611964}

  WCCI 2024",

}

Genetic Programming entries for Lin Liu Yuan Yuan

Citations