📅 17 March 2024
DOI: 10.62951/ijamc.v1i1.1
A Hybrid Optimization Approach for Non Linear Function Approximation in High Dimensional Spaces
International Journal of Applied Mathematics and Computing
Asosiasi Riset Ilmu Matematika dan Sains Indonesia (ARIMSI)
📄 Abstract
This paper introduces a hybrid optimization approach that combines genetic algorithms with gradient descent for effective nonlinear function approximation in highdimensional data. Traditional methods struggle with computational efficiency and accuracy in such complex spaces. By integrating genetic algorithms to provide a global search strategy with gradient descent for finetuning, the proposed method achieves faster convergence and improved accuracy. Simulations and case studies demonstrate its effectiveness in applications like data mining, image recognition, and financial modeling.
🔖 Keywords
#Hybrid optimization; nonlinear approximation; highdimensional data
#genetic algorithm
#gradient descent.
ℹ️ Informasi Publikasi
Tanggal Publikasi
17 March 2024
Volume / Nomor / Tahun
Volume 1, Nomor 1, Tahun 2024
📝 HOW TO CITE
Achmad Rifai; Sesi Herawani; Mery Windya Pramita, "A Hybrid Optimization Approach for Non Linear Function Approximation in High Dimensional Spaces," International Journal of Applied Mathematics and Computing, vol. 1, no. 1, Mar. 2024.
ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
0
Sitasi
Cek Google Scholar