Solution of multi-objective min-max and max-min games by evolution

In this paper, a multi-objective optimal interception problem is proposed and solved using a Multi-Objective Evolutionary Algorithm. The traditional setting of an interception engagement between pursuer and evader is targeted either at minimizing a miss distance for a given interception duration or at minimizing an interception time for a given miss distance. Such a setting overlooks an important aspect - the purpose of launching the evader in the first place. Naturally, the evader seeks to evade the pursuer (by keeping away from it), but what about hitting its target? In contrast with the traditional setting, in this paper a multi-objective game is played between a pursuer and an evader. The pursuer aims at keeping a minimum final distance between itself and the evader, which it attempts to keep away from its target. The evader, on the other hand, aims at coming as close as possible to a predefined target while keeping as far away as possible from the pursuer. Both players (pursuer and evader) utilize neural net controllers that evolve during the proposed evolutionary optimization. The game is shown to involve very interesting issues related to the decision-making process while the dilemmas of both opponents are taken into consideration.