Computational Synthesis of Multi-Domain Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{ZhunFan:2003:AAAI,
  author =       "Zhun Fan and Kisung Seo and Ronald C. Rosenberg and 
                 Jianjun Hu and Erik D. Goodman",
  title =        "Computational Synthesis of Multi-Domain Systems",
  booktitle =    "Proceedings of the 2003 AAAI Spring Symposium -
                 Computational Synthesis: From Basic Building Blocks to
                 High Level Functionality",
  year =         "2003",
  pages =        "59--66",
  address =      "Stanford, California",
  month =        mar,
  organisation = "AAAI",
  email =        "hujianju@msu.edu, goodman@egr.msu.edu",
  keywords =     "genetic algorithms, genetic programming, bond graphs,
                 evolutionary synthesis",
  URL =          "http://garage.cse.msu.edu/papers/GARAGe03-03-02.pdf",
  abstract =     "Several challenging issues have to be addressed for
                 automated synthesis of multi-domain systems. First,
                 design of interdisciplinary (multi-domain) engineering
                 systems, such as mechatronic systems, differs from
                 design of single-domain systems, such as electronic
                 circuits, mechanisms, and fluid power systems, in part
                 because of the need to integrate the several distinct
                 domain characteristics in predicting system behavior.
                 Second, a mechanism is needed to automatically select
                 useful elements from the building block repertoire,
                 construct them into a system, evaluate the system and
                 then reconfigure the system structure to achieve better
                 performance. Dynamic system models based on diverse
                 branches of engineering science can be expressed using
                 the notation of bond graphs, based on energy and
                 information flow. One may construct models of
                 electrical, mechanical, magnetic, hydraulic, pneumatic,
                 thermal, and other systems using only a rather small
                 set of ideal elements as building blocks. Another
                 useful tool, genetic programming, is a powerful method
                 for creating and evolving novel design structures in an
                 open-ended manner. Through definition of a set of
                 constructor functions, a genotype tree is created for
                 each individual in each generation. The process of
                 evaluating the genotype tree maps the genotype into a
                 phenotype -- i.e., to the abstract topological
                 description of the design of a multi-domain system,
                 using a bond graph along with parameters for each
                 component, if needed. Finally, physical realization is
                 carried out to relate each abstract element of the bond
                 graph to corresponding components in various physical
                 domains. To implement the above GPBG approach in a
                 specific application domain, cautious steps have to be
                 taken to make the evolved design represented by bond
                 graphs realizable and manufacturable. To achieve this,
                 one important step is to define appropriate building
                 blocks of the design space and carefully design a
                 realizable function set in genetic programming. We are
                 going to illustrate this in an example of behavioral
                 synthesis of an RF MEM circuit C a micro-mechanical
                 band pass filter design. Finally, we have some
                 discussions on how to extend the above approach to an
                 integrated evolutionary synthesis environment for MEMS
                 across a variety of design layers.",
}

Genetic Programming entries for Zhun Fan Kisung Seo Ronald C Rosenberg Jianjun Hu Erik Goodman

Citations