Genetic programming for stack filters

author = "E. Howard N. Oakley",

title = "Genetic programming for stack filters",

booktitle = "Handbook of Evolutionary Computation",

publisher = "Oxford University Press",

publisher_2 = "Institute of Physics Publishing",

year = "1997",

editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz",

chapter = "section G3.1",

keywords = "genetic algorithms, genetic programming, FIR",

ISBN = "0-7503-0392-1",

URL = "

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",

broken = "doi:10.1201/9781420050387.ptg",

URL = "

https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921",

size = "5 pages",

abstract = "A range of techniques was used to search for the fittest filter to remove noise from data from a blood flow measurement system. Filter types considered included finite impulse response (FIR), RC (exponential), a generalized FIR form, and stack filters. Techniques used to choose individual filters were heuristic, the genetic algorithm, and genetic programming. The efficacy of filters was assessed by measuring a fitness function, derived from the root mean square error. The fittest filter found was a stack filter, generated by genetic programming. It outperformed heuristically found median filters, and an FIR filter first produced by the genetic algorithm and then improved by genetic programming. Genetic programming proved to be an inexpensive and effective tool for the selection of an optimal filter from a class of filters which is particularly difficult to optimize. Its value in signal processing is confirmed by its ability to further improve filters created by other methods. Its main limitation is that it is, at present, too computationally intensive to be used for on-line adaptive filtering.",

notes = "blood flow",