Noisy function evaluation and the delta-coding algorithm

Keith E. Mathias; L. Darrell Whitley

doi:10.1117/12.179240

Abstract

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.

Citation Download Citation

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

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