Evolution of an artificial neural network based autonomous landvehicle controller
Baluja, S.
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Volume 26, Issue 3, Jun 1996 Page(s):450 - 463
Digital Object Identifier 10.1109/3477.499795
Summary:This paper presents an evolutionary method for creating an
artificial neural network based autonomous land vehicle controller. The
evolved controllers perform better in unseen situations than those
trained with an error backpropagation learning algorithm designed for
this task. In this paper, an overview of the previous connectionist
based approaches to this task is given, and the evolutionary algorithms
used in this study are described in detail. Methods for reducing the
high computational costs of training artificial neural networks with
evolutionary algorithms are explored. Error metrics specific to the task
of autonomous vehicle control are introduced; the evolutionary
algorithms guided by these error metrics reveal improved performance
over those guided by the standard sum-squared error metric. Finally,
techniques for integrating evolutionary search and error backpropagation
are presented. The evolved networks are designed to control Carnegie
Mellon University's NAVLAB vehicles in road following tasks
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