2 May 2007 Using a multi-agent evidential reasoning network as the objective function for an evolutionary algorithm
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Abstract
A culturally diverse group of people are now participating in military multinational coalition operations (e.g., combined air operations center, training exercises such as Red Flag at Nellis AFB, NATO AWACS), as well as in extreme environments. Human biases and routines, capabilities, and limitations strongly influence overall system performance; whether during operations or simulations using models of humans. Many missions and environments challenge human capabilities (e.g., combat stress, waiting, fatigue from long duty hours or tour of duty). This paper presents a team selection algorithm based on an evolutionary algorithm. The main difference between this and the standard EA is that a new form of objective function is used that incorporates the beliefs and uncertainties of the data. Preliminary results show that this selection algorithm will be very beneficial for very large data sets with multiple constraints and uncertainties. This algorithm will be utilized in a military unit selection tool.
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Robert Woodley, Robert Woodley, Eric Lindahl, Eric Lindahl, Joseph Barker, Joseph Barker, } "Using a multi-agent evidential reasoning network as the objective function for an evolutionary algorithm", Proc. SPIE 6563, Evolutionary and Bio-inspired Computation: Theory and Applications, 65630I (2 May 2007); doi: 10.1117/12.718395; https://doi.org/10.1117/12.718395
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