Evolved neurogenesis and synaptogenesis for robotic control: the L-brain model

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  author =       "Michael E. Palmer",
  title =        "Evolved neurogenesis and synaptogenesis for robotic
                 control: the L-brain model",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1515--1522",
  keywords =     "genetic algorithms, genetic programming, Generative
                 and developmental systems",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001780",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We have developed a novel method to grow neural
                 networks according to an inherited set of production
                 rules (the genotype), inspired by Lindenmayer systems.
                 In the first phase (neurogenesis), the neurons
                 proliferate in three-dimensional space by cell
                 division, and differentiate in function, according to
                 the production rules. In the second phase
                 (synaptogenesis), axons emerge from the neurons and
                 seek out connection targets. Part of each production
                 rule is an augmented Reverse Polish Notation
                 expression; this permits regulation of the applicable
                 rules, as well as introduction of spatial and temporal
                 context to the developmental process. We connect each
                 network to a (fixed) robotic body with a set of input
                 sensors and muscle actuators. The robot is placed in a
                 physically simulated environment and controlled by its
                 network for a certain time, receiving a fitness score
                 according to its behavior (the phenotype). Mutations
                 are introduced into offspring by making changes to
                 their sets of production rules. This paper introduces
                 the L-brain developmental method, and describes our
                 first experiments with it, which produced controllers
                 for robotic spiders with the ability to gallop, and to
                 follow a compass heading.",
  notes =        "Also known as \cite{2001780} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",

Genetic Programming entries for Michael E Palmer