Generative Representations for Evolutionary Design Automation

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Gregory Scott Hornby",
  title =        "Generative Representations for Evolutionary Design
  school =       "Brandeis University, Dept. of Computer Science",
  year =         "2003",
  address =      "Boston, MA, USA",
  month =        feb,
  email =        "",
  keywords =     "genetic algorithms, genetic programming, generative
                 representation, evolutionary design",
  URL =          "",
  broken =       "",
  URL =          "",
  abstract =     "In this thesis the class of generative representations
                 is defined and it is shown that this class of
                 representations improves the scalability of
                 evolutionary design systems by automatically learning
                 inductive bias of the design problem thereby capturing
                 design dependencies and better enabling search of large
                 design spaces. First, properties of representations are
                 identified as: combination, control-flow, and
                 abstraction. Using these properties, representations
                 are classified as non-generative, or generative.
                 Whereas non-generative representations use elements of
                 encoded artifacts at most once in translation from
                 encoding to actual artifact, generative representations
                 have the ability to reuse parts of the data structure
                 for encoding artifacts through control-flow (using
                 iteration) and/or abstraction (using labelled
                 procedures). Unlike non-generative representations,
                 which do not scale with design complexity because they
                 cannot capture design dependencies in their structure,
                 it is argued that evolution with generative
                 representations can better scale with design complexity
                 because of their ability to hierarchically create
                 assemblies of modules for reuse, thereby enabling
                 better search of large design spaces. Second, GENRE, an
                 evolutionary design system using a generative
                 representation, is described. Using this system, a
                 non-generative and a generative representation are
                 compared on four classes of designs: three-dimensional
                 static structures constructed from voxels; neural
                 networks; actuated robots controlled by oscillator
                 networks; and neural network controlled robots. Results
                 from evolving designs in these substrates show that the
                 evolutionary design system is capable of finding
                 solutions of higher fitness with the generative
                 representation than with the non-generative
                 representation. This improved performance is shown to
                 be a result of the generative representation's ability
                 to capture intrinsic properties of the search space and
                 its ability to reuse parts of the encoding in
                 constructing designs. By capturing design dependencies
                 in its structure, variation operators are more likely
                 to be successful with a generative representation than
                 with a non-generative representation. Second, reuse of
                 data elements in encoded designs improves the ability
                 of an evolutionary algorithm to search large design
  size =         "242 pages",
  notes =        "Fri, 10 Sep 2004 01:13:34 EDT
        GENREv1.1b source

Genetic Programming entries for Gregory S Hornby