Describes the development of a software tool for optimizing large-scale air combat tactics using stochastic genetic algorithms. The tool integrates four key components: (1) autonomous blue/red player agents, with their individual aircraft and tactics; (2) an engagement simulator used to play out a tactical scenario; (3) performance metrics reflecting engagement outcome and tactical advantage; and (4) a GA "engine" for performance-based optimization of blue team tactics. The tool's capabilities are demonstrated through the optimization of blue team formation and intercept geometry in a series of tactical engagements. The tactics implementation uses a hierarchical concept that builds large formation tactics from small conventional fighting units, facilitating the design of tactics compatible with existing air combat principles.

This paper investigates the potential benefit of elastic energy storage in the propulsion of a swimming robot. A discussion of the hydrodynamic models of von Karman and Sears (1938) and Lighthill (1969, 1970) is presented and is followed by an analysis of the input power required for cases with and without elastic energy storage. It is concluded that the addition of linear springs to the system provides a means to reduce the energy required of the power supply. A transfer function for the system is also derived for purposes of controlling a swimming robot.