Allocation of Forces, Fires, and Effects Using Genetic Algorithms
ResearchPublished Jun 11, 2008
ResearchPublished Jun 11, 2008
Decisionmaking within the Future Battle Command structure will demand an increasing ability to comprehend and structure information on the battlefield. As the military evolves into a networked force, headquarters and others must collect and utilize information from across the battlefield in a timely and efficient manner. Decision aids and solution methodologies in constructive simulations must be modified to better show how this information affects decisions. Using information about friendly and enemy forces and the terrain, a RAND-developed model that incorporates a genetic algorithm (1) determines preferred Blue routes around Red forces and (2) allocates forces to these routes. This model is unique in its incorporation of many higher-level intelligence products — including intelligence about Red's location, activity, intent, military capability, intelligence capability, and adaptability — into the planning algorithm. The integration of these products allows the model to produce sophisticated look-ahead representations of enemy forces that are superior to the static representations typically used in planning sessions. The model also features terrain representations that measure impassibility, inhospitableness, and shadowing, allowing planners to transcend scenarios that assume a lack of interesting terrain.
The research described in this report was sponsored by the United States Army and conducted by RAND Arroyo Center.
This publication is part of the RAND technical report series. RAND technical reports, products of RAND from 2003 to 2011, presented research findings on a topic limited in scope or intended for a narrow audience; discussions of the methodology employed in research; literature reviews, survey instruments, modeling exercises, guidelines for practitioners and research professionals, and supporting documentation; and preliminary findings. All RAND technical reports were subject to rigorous peer review to ensure high standards for research quality and objectivity.
This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.
RAND is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.