DocumentCode
2918189
Title
Evolutionary spiking neural networks as racing car controllers
Author
Yee, Elias ; Teo, Jason
Author_Institution
Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah, Kota Kinabalu, Malaysia
fYear
2011
fDate
5-8 Dec. 2011
Firstpage
411
Lastpage
416
Abstract
The Izhikevich spiking neural network model is investigated as a method to develop controllers for a simple, but not trivial, car racing game, called TORCS. The controllers are evolved using Evolutionary Programming, and the performance of the best individuals is compared with the hand-coded controller included with the Simulated Car Racing Championship API. The results are promising, indicating that this neural network model can be applied to other games or control problems.
Keywords
automobiles; evolutionary computation; neurocontrollers; API; Izhikevich spiking neural network model; TORCS; car racing game; evolutionary programming; evolutionary spiking neural network; hand-coded controller; racing car controller; simulated car racing championship; Biological neural networks; Biological system modeling; Computational modeling; Electric potential; Games; Mathematical model; Neurons; Izhikevich neuron model; Spiking neural networks; TORCS; car racing; evolutionary programming; games;
fLanguage
English
Publisher
ieee
Conference_Titel
Hybrid Intelligent Systems (HIS), 2011 11th International Conference on
Conference_Location
Melacca
Print_ISBN
978-1-4577-2151-9
Type
conf
DOI
10.1109/HIS.2011.6122141
Filename
6122141
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