- 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://gpbib.cs.ucl.ac.uk/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, neural networks, ANN, 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",
