Stochastic training of a biologically plausible spino-neuromuscular system model

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

@InProceedings{1277011,
  author =       "Stanley Phillips Gotshall and Terence Soule",
  title =        "Stochastic training of a biologically plausible
                 spino-neuromuscular system model",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "1",
  isbn13 =       "978-1-59593-697-4",
  pages =        "253--260",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p253.pdf",
  DOI =          "doi:10.1145/1276958.1277011",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 Life, Evolutionary Robotics, Adaptive Behaviour,
                 Evolvable Hardware, breeding swarm optimisers, genetic
                 algorithms, neural networks, particle swarm optimiser,
                 spiking networks, spinal cord",
  abstract =     "A primary goal of evolutionary robotics is to create
                 systems that are as robust and adaptive as the human
                 body. Moving toward this goal often involves training
                 control systems that process sensory information in a
                 way similar to humans. Artificial neural networks have
                 been an increasingly popular option for this because
                 they consist of processing units that approximate the
                 synaptic activity of biological signal processing
                 units, i.e. neurons. In this paper we train a nonlinear
                 recurrent spino-neuromuscular system (SNMS) model and
                 compare the performance of genetic algorithms (GA)s,
                 particle swarm optimisers (PSO)s, and GA/PSO hybrids.
                 Several key features of the SNMS model have previously
                 been modelled individually but have not been combined
                 into a single model as is done here. The results show
                 that each algorithm produces fit solutions and
                 generates fundamental biological behaviours, such as
                 tonic tension behaviors and triceps activation
                 patterns, that are not explicitly trained.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for Stanley Phillips Gotshall Terence Soule

Citations