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A new methodology for optimizing evasive maneuvers under uncertainty in the extended two-dimensional pursuer/evader problem

Traditional analytic or control-theoretic solutions to the problem of optimizing evasive maneuvers in the extended two-dimensional pursuer/evader problem require the evader to execute specific sequences of maneuvers at precise pursuer/evader distances. These solutions depend upon several pursuer-specific characteristics, and fail to effectively account for uncertainty about the state of the pursuer. This paper describes the implementation of a genetic programming system that evolves optimized solutions to the extended two-dimensional pursuer/evader problem that do not depend upon knowledge of the pursuer's current state. Best-of-run programs execute strategies by which an evader may maneuver to successfully evade a pursuer starting from a wide range of relative initial positions, under conditions where the state of the pursuer is unknown or uncertain.