- author = "Howard Oakley",
- title = "Two Scientific Applications of Genetic Programming: Stack Filters and Non-Linear Equation Fitting to Chaotic Data",
- booktitle = "Advances in Genetic Programming",
- publisher = "MIT Press",
- editor = "Kenneth E. {Kinnear, Jr.}",
- year = "1994",
- pages = "369--389",
- chapter = "17",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.amazon.co.uk/Advances-Genetic-Programming-Complex-Adaptive/dp/0262111888",
-
URL = "
http://cognet.mit.edu/sites/default/files/books/9780262277181/pdfs/9780262277181_chap17.pdf",
- size = "21 pages",
- abstract = "Optimisation within nearly infinite search space is a common problem in applied science, for which two examples illustrate the application of genetic programming. Three different techniques were used to develop filters for the removal of noise from experimental data. Heuristic search was used to develop a median filter, a classical genetic algorithm optimized a 7-tap moving average (FIR) filter, and genetic programming was used to optimize a stack filter. The latter had the highest fitness, and was computationally more efficient than the best median filter, which was in turn superior in fitness to the best moving average filter. Genetic programming was also used to fit empirical equations to a chaotic time-series (the Mackey-Glass equation) and non-linear physiological data. Initial results confirm the key role of the fitness measure in such work; oscillatory series are readily fitted with linear functions unless the computation of fitness includes an appropriate measure such as incremental comparison of Fourier power series. The use of Lyapunov exponents and dimension estimation is suggested in more sophisticated compound fitness measures. Genetic programming may prove to be useful in both forecasting and structural studies of non-linear systems, at both local and global levels.",
-
notes = "Two Scientific Applications of Genetic Programming:
The development of stack filters, the fitting of
non-linear equations to chaotic data
Mackey-Glass, REAL World examples. Contrasts with other techniques eg GA.
",
