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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Palmer:2011:GECCO,

• 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

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