Non-linear Principal Components Analysis Using Genetic Programming   [GP]

by

Hiden, H., G., Hiden, H., Willis, M., J., Willis, M., Turner, P., Tham, M., Tham, M., T., Montague, G., A. and Montague, G.

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Info: 1996
Keywords:genetic algorithms, genetic programming
Abstract:
The recent explosion of low-cost computing power and information storage has brought with it a corresponding mushrooming in the amount of data on almost any subject conceivable that is available. The philosophy that "you can?t have enough information" seems to have been applied to every situation with great enthusiasm. By adopting such an approach, much useful data can be gathered, however it is all too frequently swamped by irrelevant information. The distinction must be made between useful information and information for the sake of having it. The chemical industry also has not been immune to the data collection bug. The equipment required to collect, process and store data is more affordable than ever, a fact which the designers of chemical processes are beginning to exploit. Unfortunately, this data is not particularly useful on its own. It is very easy to collect data, but difficult to analyse it productively. It is this situation that has spawned a wide variety of data analysis tools, [DA] the objective of which is to determine underlying relationships and structures within large data sets. [DS]
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BibTex:
@TechReport{hiden:1996:npcaGP,
  author =       "H. G. Hiden and M. J. Willis and P. Turner and M. T.
                 Tham and G. A. Montague",
  title =        "Non-linear Principal Components Analysis Using Genetic
                 Programming",
  institution =  "Chemical Engineering, Newcastle University",
  year =         "1996",
  address =      "UK",
  note =         "Extended Abstract, ICANNGA '97, Norwick, UK",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper9a.ps",
  abstract =     "

                 The recent explosion of low-cost computing power and
                 information storage has brought with it a corresponding
                 mushrooming in the amount of data on almost any subject
                 conceivable that is available. The philosophy that
                 {"}you can?t have enough information{"} seems to have
                 been applied to every situation with great enthusiasm.
                 By adopting such an approach, much useful data can be
                 gathered, however it is all too frequently swamped by
                 irrelevant information. The distinction must be made
                 between useful information and information for the sake
                 of having it. The chemical industry also has not been
                 immune to the data collection bug. The equipment
                 required to collect, process and store data is more
                 affordable than ever, a fact which the designers of
                 chemical processes are beginning to exploit.
                 Unfortunately, this data is not particularly useful on
                 its own. It is very easy to collect data, but difficult
                 to analyse it productively. It is this situation that
                 has spawned a wide variety of data analysis tools, the
                 objective of which is to determine underlying
                 relationships and structures within large data sets.",
  notes =        "MSword postscript not compatible with unix.

                 ",
}