Neuro-evolution using recombinational algorithms and embryogenesis for robotic control

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

@PhdThesis{AnthonyMRoy:thesis,
  author =       "Anthony M. Roy",
  title =        "Neuro-evolution using recombinational algorithms and
                 embryogenesis for robotic control",
  school =       "Engineering and Applied Science, California Institute
                 of Technology",
  year =         "2009",
  address =      "USA",
  month =        "11 " # dec,
  keywords =     "genetic algorithms, genetic programming, ANN, Neural
                 Network, Robotics, Artificial Intelligence",
  URL =          "http://thesis.library.caltech.edu/5944/",
  URL =          "http://thesis.library.caltech.edu/5944/1/main.pdf",
  size =         "192 pages",
  abstract =     "Control tasks involving dramatic nonlinearities, such
                 as decision making, can be challenging for classical
                 design methods. However, autonomous, stochastic design
                 methods such as evolutionary computation have proved
                 effective. In particular, genetic algorithms that
                 create designs via the application of recombinational
                 rules are robust and highly scalable. Neuro-Evolution
                 Using Recombinational Algorithms and Embryogenesis
                 (NEURAE) is a genetic algorithm that creates C++
                 programs that in turn create neural networks which can
                 function as logic gates. The neural networks created
                 are scalable and robust enough to feature redundancies
                 that allow the network to function despite internal
                 failures. An analysis of NEURAE evinces how
                 biologically inspired phenomena apply to simulated
                 evolution. This allows for an optimisation of NEURAE
                 that enables it to create controllers for a simulated
                 swarm of Khepera-inspired robots.",
  notes =        "Antonsson, Erik K. (co-advisor) Shapiro, Andrew A.
                 (co-advisor) Burdick, Joel Wakeman (advisor)",
}

Genetic Programming entries for Anthony M Roy

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