Abstract
Simulation modeling the battlefield scenario should provide a realistic training ground for the soldiers where it is possible to test the soldiers' skills in a variety of situations. The design of opponents is one of significant facts to influence train level in battlefield simulation. This paper endeavors to show how method as multipopulation genetic algorithms can be used to address the problems such as how to make opponents' actions and strategies unpredictable and how to make battlefield simulation circumstance more realistic. Multipopulation genetic algorithms' inherent optimizing characteristic in subpopulations is just adaptive to solving our problem. The origin of this work is in the area of military training in battlefield simulation.
Published Version
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