ABSTRACT

To solve the extended two-dimensional pursuer/evader problem, a strategy must be identified by which an evader (such as an F-16C fighter aircraft) may maneuver to successfully evade pursuers (such as surface-to-air missiles) launched from a wide range of potentially lethal relative initial positions. Uncertainty about the type of pursuer introduces a degree of uncertainty that is difficult to model using traditional analytic or control-theoretic approaches. This paper describes the implementation of a genetic programming system that uses training populations reflecting specific probability distributions to evolve optimized solutions to the extended two-dimensional pursuer/evader problem under conditions of uncertainty about the type of pursuer.