- author = "Birkan Can",
- title = "Evolutionary Modelling of Industrial Systems with Genetic Programming",
- school = "University of Limerick",
- year = "2011",
- type = "Doctorate of Philosophy in Engineering",
- address = "Ireland",
- month = oct,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://hdl.handle.net/10344/1693",
-
URL = "http://ulir.ul.ie/handle/10344/1693",
-
URL = "
http://ulir.ul.ie/bitstream/handle/10344/1693/2010_Birkan%2c%20Can.pdf",
- size = "216 pages",
-
abstract = "Knowledge, experience, and intuition are integral
parts of decision making. However, these alone are not
sufficient 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 system. In this respect, the dissertation
develops approximate models, metamodels, of industrial
systems to facilitate a means to quantify system
performance when the trade-off between approximation
error and efficiency (time and effort spent on model
development, validation, maintenance and execution) is
appropriate. Discrete-event simulation (DES) is widely
used to assist decision makers in the management of
systems. DES facilitates analysis with high fidelity
models as a consequence of its flexibility. However,
this descriptiveness introduces an overhead to model
building and maintenance. Furthermore, due to
stochastic elements and the size of the systems
modelled, model execution times can be computationally
demanding. Hence, its use in operational tasks such as
design, sensitivity analysis and optimisation can be
significantly undermined when efficiency is a concern.
In this thesis, these shortcomings are addressed
through research into the use of genetic programming
for metamodelling.
Genetic programming 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. Furthermore, genetic programming develops the models without prior assumptions about the underlying function of the training data. This can provide significant advantage for modelling of complex systems with non-linear and multimodal response characteristics. Exploiting these properties, the dissertation presents research towards developing metamodels of manufacturing systems (or their DES models) via genetic programming in the context of symbolic regression. In particular, it contributes to; (i) exploration of an appropriate experimental design method suitable to use with genetic programming, (ii) to a comparison of the performance of genetic programming with neural networks, using three different stochastic industrial problems to identify its robustness; (iii) research into an improved genetic programming and dynamic flow time estimation.",
-
notes = "2011 Supervisor Dr. Cathal Heavey.
'thesis is available in the University Library'
",
