%0 Conference Paper %1 1277011 %A Gotshall, Stanley Phillips %A Soule, Terence %B GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation %C London %D 2007 %E Thierens, Dirk %E Beyer, Hans-Georg %E Bongard, Josh %E Branke, Jurgen %E Clark, John Andrew %E Cliff, Dave %E Congdon, Clare Bates %E Deb, Kalyanmoy %E Doerr, Benjamin %E Kovacs, Tim %E Kumar, Sanjeev %E Miller, Julian F. %E Moore, Jason %E Neumann, Frank %E Pelikan, Martin %E Poli, Riccardo %E Sastry, Kumara %E Stanley, Kenneth Owen %E Stutzle, Thomas %E Watson, Richard A %E Wegener, Ingo %I ACM Press %K Adaptive Artificial Behaviour, Evolutionary Evolvable Hardware, Life, Robotics, algorithms, breeding cord genetic networks, neural optimiser, optimisers, particle programming, spiking spinal swarm %P 253--260 %T Stochastic training of a biologically plausible spino-neuromuscular system model %U http://doi.acm.org/10.1145/1276958.1277011 %V 1 %X 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.

@inproceedings{1277011, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {London}, author = {Gotshall, Stanley Phillips and Soule, Terence}, biburl = {https://www.bibsonomy.org/bibtex/279dad86f8c771f9a51b203eae6d4b422/brazovayeye}, booktitle = {GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation}, editor = {Thierens, Dirk and Beyer, Hans-Georg and Bongard, Josh and Branke, Jurgen and Clark, John Andrew and Cliff, Dave and Congdon, Clare Bates and Deb, Kalyanmoy and Doerr, Benjamin and Kovacs, Tim and Kumar, Sanjeev and Miller, Julian F. and Moore, Jason and Neumann, Frank and Pelikan, Martin and Poli, Riccardo and Sastry, Kumara and Stanley, Kenneth Owen and Stutzle, Thomas and Watson, Richard A and Wegener, Ingo}, interhash = {c4b05802da5aac2a7856a2761adf6176}, intrahash = {79dad86f8c771f9a51b203eae6d4b422}, isbn13 = {978-1-59593-697-4}, keywords = {Adaptive Artificial Behaviour, Evolutionary Evolvable Hardware, Life, Robotics, algorithms, breeding cord genetic networks, neural optimiser, optimisers, particle programming, spiking spinal swarm}, month = {7-11 July}, 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}, organisation = {ACM SIGEVO (formerly ISGEC)}, pages = {253--260}, publisher = {ACM Press}, publisher_address = {New York, NY, USA}, timestamp = {2008-06-19T17:40:30.000+0200}, title = {Stochastic training of a biologically plausible spino-neuromuscular system model}, url = {http://doi.acm.org/10.1145/1276958.1277011}, volume = 1, year = 2007 }