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Evolutionary Modelling of Industrial Systems
with Genetic Programming
Dr Birkan Can, Dr Cathal Heavey
Enterprise Research Centre University of Limerick
birkan.can@ul.ie, cathal.heavey@ul.ie
Abstract
To manage today’s industrial systems, often predictive
models are required to weigh options and determine
potential changes which provide the best outcome for a
purpose. In this research, we investigate genetic
programming to develop tangible analytic models of
system performance dependent on decision variables to
support decision making
1. Introduction
Management of complex systems, such as in
semiconductor manufacturing, requires predictive
models. These quantify the impact of decisions on
system performance prior to changes. Companies like
Seagate, Analog Devices and Intel struggle with being
able to predict changes,
In this respect, this research develops approximate
models, metamodels, of industrial systems using genetic
programming (GP) to facilitate a means to quantify the
performance when the trade-off between approximation
error and efficiency is appropriate.
2. Predictive Models
The overview of industry and relevant literature has
revealed several prominent tools in application and
some of their undesired characteristics.
Analytical models are computationally efficient
methods, however, they may require restrictive
assumptions to make complex systems more tractable
for modeling. Spreadsheet simulations are primarily
used; but, provide low fidelity models as they do not
capture the dynamics of the system. In contrast,
discrete-event simulation (DES) can provide higher
fidelity models; but are very difficult to develop and
maintain and in many cases take too long to execute to
support planning (McNally and Heavey, 2004).
In the next, we summarise GP to develop explicit
approximate predictive models.
3. Main title
GP is a branch of evolutionary algorithms which
emulate the natural evolution of species. It can evolve
programs of a domain via symbolic regression. These
programs can be interpreted as logic instructions,
analytical functions etc. GP develops the models
without prior assumptions about the underlying
function of the training data. These properties provide a
substantial advantage for modelling of complex systems
with GP. In the following, we provide the results from
the analysis of application of GP in this context.
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4. Robustness and Competitiveness
Table 1 - Analysis on different problems
Problem SF1 SF2 SF3 Time
6 station 0.9840 0.9834 0.9856 10m
9 station 0.9778 0.9811 0.9784 15m
12 station 0.9856 0.9860 0.9846 15m
We tested accuracy of GP on 3 different production
lines, where training data is collected with different
space-filling experimental design methods. Results
indicate GP is robustness to problem size and data
collection (Table 1).
Table 2 - Comparison with ANNs
Problem ANN GP
AMHS 0.79 0.85
sS 0.83 0.93
9 station 0.91 0.99
We compared the performance of GP to artificial
neural networks (ANNs) since they are extensively
used in metamodelling. GP performed with a better
accuracy on un-seen data (Table 2). Furthermore, the
explicit functions are delivered which are easy to
interpret compared the ANN metamodels.
6. A GP for Dynamic Modelling
Table 3 - Table 4 - Analysis on different problems
Problem SF1 SF2 SF3 Time
6 station 0.9946 0.9939 0.9935 2m
9 station 0.9948 0.9959 0.9942 2m
12 station 0.9955 0.9958 0.9961 2m
We manipulated the standard GP algorithm with
new constraints to improve its accuracy and
development time of metamodels. Table 3 indicates that
GP has obtained significant gains through these
changes.
7. Conclusion
In this manuscript, the results from application of
genetic programming to metamodelling of industrial
systems are summarised. Genetic programming has
yield approximate models reaching beyond 98%
accuracy efficiently. The results suggest incentive
towards developing dynamic models using GP.