6 April 2000 Genetic-algorithm-based optimization of a fuzzy logic resource manager for electronic attack
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Abstract
A fuzzy logic based expert system has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar platforms. The platforms can be very general, e.g., ships, planes, robots, land based facilities, etc. Potential foes the platforms deal with can also be general. This paper describes data mining activities related to development of the resource manager with a focus on genetic algorithm based optimization. A genetic algorithm requires the construction of a fitness function, a function that must be maximized to give optimal or near optimal results. The fitness functions are in general non- differentiable at many points and highly non-linear, neither property providing difficulty for a genetic algorithm. The fitness functions are constructed using insights from geometry, physics, engineering, and military doctrine. Examples are given as to how fitness functions are constructed including how the fitness function is averaged over a database of military scenarios. The use of a database of scenarios prevents the algorithm from having too narrow a range of behaviors, i.e., it creates a more robust solution.
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James F. Smith, Robert D. Rhyne, "Genetic-algorithm-based optimization of a fuzzy logic resource manager for electronic attack", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); doi: 10.1117/12.381764; https://doi.org/10.1117/12.381764
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