A comparison of two Genetic Programming Algorithms Applied to Chemical Process Systems Modelling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @TechReport{hinchcliffe:1996:c2GPcpsm,
-
author = "Mark Hinchliffe and Mark Willis and Hugo Hiden and
Ming Tham",
-
title = "A comparison of two Genetic Programming Algorithms
Applied to Chemical Process Systems Modelling",
-
institution = "Chemical Engineering, Newcastle University",
-
year = "1996",
-
address = "UK",
-
note = "Extended Abstract, submitted to: ICANNGA '97, Norwick,
UK",
-
keywords = "genetic algorithms, genetic programming",
-
broken = "http://lorien.ncl.ac.uk/sorg/paper10a.ps",
-
abstract = "Previous work by McKay et al (1996a,b,c) has shown
that the Genetic programming (GP) methodology can be
successfully applied to the development of non-linear
steady state models of industrial chemical processes.
Although a GP algorithm can identify the relevant input
variables and evolve parsimonious system
representations, the resulting model structures tend to
contain little or no information relating to the
mechanisms of the process itself. In this respect, the
performance of the GP methodology is comparable to that
of other black-box modelling techniques such as neural
networks. Chemical process systems are often extremely
complex and non-linear in nature. Phenomenological
models are time consuming to develop and can be subject
to inaccuracies caused by any simplifying assumptions
made. Consequently, mechanistic models are costly to
construct; an aspect which would make an automated
procedure highly desirable. Phenomenological models are
usually derived by applying the laws of conservation of
mass, energy and momentum to the system. An examination
of a number of steady-state mechanistic models shows
that they tend to be made up of distinct sub-groups
which, when added together, give the overall model
structure. In the search for an automatic model
generating algorithm, it would be extremely useful if
the GP methodology could be used to identify these
sub-groups. This could potentially enhance the GP
algorithm's ability to evolve accurate chemical process
models and also help to reveal hidden process
knowledge. To achieve this goal, the standard GP
algorithm used by McKay et al (1996a) was modified to
accommodate the multiple gene model structure. The
multiple gene structure was introduced by Altenberg
(1994) in an attempt to enhance the learning
capabilities of GA and GP algorithms. The work was
inspired by the observation that, in nature, genetic
information is stored on more than one gene. To
demonstrate the feasibility of this new approach, real
world examples are used to compare the performance of
the algorithm with that of the standard GP algorithm.",
-
notes = "MSword postscript not camptible with unix",
-
size = "7 pages",
- }
Genetic Programming entries for
Mark P Hinchliffe
Mark J Willis
Hugo Hiden
Ming T Tham
Citations