Using a Multi-Agent Evidential Reasoning Network as the Objective Function for an Evolutionary Algorithm


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. © 2007 IEEE.

Meeting Name

2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems, KIMAS 2007 (2007: Apr. 30-May 3, Waltham, MA)


Electrical and Computer Engineering

Keywords and Phrases

Case based reasoning; Computer simulation; Data structures; Evolutionary algorithms; Mathematical models, Combined air operations centers; Objective functions; Reasoning networks, Multi agent systems

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2007