Genetic algorithms are becoming increasingly popular as a tool for optimization in signal processing environments due to their tolerance for noise. Several types of genetic algorithms are compared against a mutation driven stochastic hill-climbing algorithm on a standard set of benchmark functions which have had Gaussian noise added to them. The genetic algorithms used in these comparisons include an elitist simple genetic algorithm, the CHC adaptive search algorithm, and delta coding. Finally several hybrid genetic algorithms are described and compared on a very large and noisy seismic data imaging problem.

Date Published: 30 June 1994
PDF: 12 pages
Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179240

Published in SPIE Proceedings Vol. 2304:
Neural and Stochastic Methods in Image and Signal Processing III
Su-Shing Chen, Editor(s)