Utilizing Symmetry in Evolutionary Design

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{Valsalam:thesis,
  author =       "Vinod K. Valsalam",
  title =        "Utilizing Symmetry in Evolutionary Design",
  school =       "Department of Computer Sciences, The University of
                 Texas at Austin",
  year =         "2010",
  address =      "Austin, TX, USA",
  month =        aug,
  note =         "Available as Technical Report AI-10-04",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.genetic-programming.org/hc2011/03-Valsalam/Valsalam-Text.txt",
  URL =          "http://nn.cs.utexas.edu/downloads/papers/valsalam.phdtr10.pdf",
  size =         "120 pages",
  abstract =     "Can symmetry be used as a design principle to
                 constrain evolutionary search, making it more
                 effective? This dissertation aims to show that this is
                 indeed the case, in two ways. First, an approach called
                 ENSO is developed to evolve modular neural network
                 controllers for simulated multilegged robots. Inspired
                 by how symmetric organisms have evolved in nature, ENSO
                 uses group theory to break symmetry systematically,
                 constraining evolution to explore promising regions of
                 the search space. As a result, it evolves effective
                 controllers even when the appropriate symmetry
                 constraints are difficult to design by hand. The
                 controllers perform equally well when transferred from
                 simulation to a physical robot. Second, the same
                 principle is used to evolve minimal-size sorting
                 networks. In this different domain, a different
                 instantiation of the same principle is effective:
                 building the desired symmetry step-by-step. This
                 approach is more scalable than previous methods and
                 finds smaller networks, thereby demonstrating that the
                 principle is general. Thus, evolutionary search that
                 uses symmetry constraints is shown to be effective in a
                 range of challenging applications.",
  notes =        "Is this GP?

                 Entered for 2011 HUMIES GECCO 2011
                 http://www.genetic-programming.org/combined.php

                 dissertation supervisor: Risto Miikkulainen",
}

Genetic Programming entries for Vinod K Valsalam

Citations