Evolving Nonlinear Multigrid Methods With Grammar-Guided Genetic Programming

Parthasarathy D, Schmitt J, Köstler H (2023)

Publication Type: Conference contribution

Publication year: 2023

Publisher: Association for Computing Machinery, Inc

Pages Range: 615-618

Conference Proceedings Title: GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion

Event location: Lisbon, PRT

ISBN: 9798400701207

DOI: 10.1145/3583133.3590734

Abstract

We formulate a formal grammar to generate Full Approximation Scheme multigrid solvers. Then, using Grammar-Guided Genetic Programming we perform a multiobjective optimization to find optimal instances of such solvers for a given nonlinear system of equations. This approach is evaluated for a two-dimensional Poisson problem with added nonlinearities. We observe that the evolved solvers outperform the baseline methods by having a faster runtime and a higher convergence rate.

Authors with CRIS profile

Dinesh Parthasarathy Lehrstuhl für Informatik 10 (Systemsimulation) Jonas Schmitt Technische Fakultät Harald Köstler Lehrstuhl für Informatik 10 (Systemsimulation)

How to cite

APA:

Parthasarathy, D., Schmitt, J., & Köstler, H. (2023). Evolving Nonlinear Multigrid Methods With Grammar-Guided Genetic Programming. In GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 615-618). Lisbon, PRT: Association for Computing Machinery, Inc.

MLA:

Parthasarathy, Dinesh, Jonas Schmitt, and Harald Köstler. "Evolving Nonlinear Multigrid Methods With Grammar-Guided Genetic Programming." Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion, GECCO 2023 Companion, Lisbon, PRT Association for Computing Machinery, Inc, 2023. 615-618.

BibTeX: Download