Genetic programming for iterative numerical methods

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Sobania:2022:GPEM,

• author = "Dominik Sobania and Jonas Schmitt and Harald Koestler and Franz Rothlauf",
• title = "Genetic programming for iterative numerical methods",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2022",
• volume = "23",
• number = "2",
• pages = "253--278",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Iterative numerical methods, Linear systems, Sparse linear algebra",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/cCB5J",
• DOI = "doi:10.1007/s10710-021-09425-5",
• size = "26 pages",
• abstract = "We introduce GPLS (Genetic Programming for Linear Systems) as a GP system that finds mathematical expressions defining an iteration matrix. Stationary iterative methods use this iteration matrix to solve a system of linear equations numerically. GPLS aims at finding iteration matrices with a low spectral radius and a high sparsity, since these properties ensure a fast error reduction of the numerical solution method and enable the efficient implementation of the methods on parallel computer architectures. We study GPLS for various types of system matrices and find that it easily outperforms classical approaches like the Gauss-Seidel and Jacobi methods. GPLS not only finds iteration matrices for linear systems with a much lower spectral radius, but also iteration matrices for problems where classical approaches fail. Additionally, solutions found by GPLS for small problem instances show also good performance for larger instances of the same problem.",
• notes = "Johannes Gutenberg University Mainz, Mainz, Germany",

}

Genetic Programming entries for Dominik Sobania Jonas Schmitt Harald Koestler Franz Rothlauf

Citations