30 June 1994 Noisy function evaluation and the delta-coding algorithm
Author Affiliations +
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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Keith E. Mathias, Keith E. Mathias, L. Darrell Whitley, L. Darrell Whitley, } "Noisy function evaluation and the delta-coding algorithm", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); doi: 10.1117/12.179240; https://doi.org/10.1117/12.179240


Theoretical developments in evolutionary computation
Proceedings of SPIE (October 31 1999)
Provably convergent inhomogeneous genetic annealing algorithm
Proceedings of SPIE (December 15 1992)
Global convergence of genetic algorithms
Proceedings of SPIE (December 15 1992)
Evolving neural network architecture
Proceedings of SPIE (December 15 1992)

Back to Top