Abstract
To realize design automation of dynamic systems, there
are two major issues to be dealt with: open-topology
generation of dynamic systems and simulation or
analysis of those models. For the first issue, we
exploit the strong topology exploration capability of
genetic programming to create and evolve structures
representing dynamic systems. With the help of ERCs
(ephemeral random constants) in genetic programming, we
can also evolve the sizing of dynamic system components
along with the structures. The second issue, simulation
and analysis of those system models, is made more
complex when they represent mixed-energy- domain
systems. We take advantage of bond graphs as a tool for
multi- or mixed-domain modeling and simulation of
dynamic systems. Because there are many considerations
in dynamic system design that are not completely
captured by a bond graph, we would like to generate
multiple solutions, allowing the designer more latitude
in choosing a model to implement. The approach in this
paper is capable of providing a variety of design
choices to the designer for further analysis,
comparison and trade-off. The approach is shown to be
efficient and effective in an example of open-ended
real- world dynamic system design application, a
printer re-design problem.
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