Test and Evaluation by Genetic Algorithms

Authors:
Alan C. Schultz;John J. Grefenstette;Kenneth A. De Jong
Affiliations:
-;-;-
Venue:
IEEE Expert: Intelligent Systems and Their Applications
Year:
1993

Citing 3
Cited 12

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Computational & Mathematical Organization Theory

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Abstract

A machine learning technique for automating the traditional controller tests process that evaluates autonomous-vehicle software controllers is discussed. In the proposed technique, a controller is subjected to an adaptively chosen set of fault scenarios in a vehicle simulator, and then a genetic algorithm is used to search for fault combinations that produce noteworthy actions in the controller. This approach has been applied to find a minimal set of faults that produces degraded vehicle performance and a maximal set of faults that can be tolerated without significant performance loss.