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This paper presents a dynamical system model for FGA, a force-based genetic algorithm, which is used as decentralized topology control mechanism among active running software agents to achieve a uniform spread of autonomous mobile nodes over an unknown geographical area. Using only local information, FGA guides each node to select a fitter location, speed and direction among exponentially large number of choices, converging towards a uniform node distribution. By treating a genetic algorithm (GA) as a dynamical system we can analyze it in terms of its trajectory in the space of possible populations. We use Vose's theoretical model to calculate the cumulative effects of GA operators of selection, mutation, and crossover as a population evolves through generations. We show that FGA converges toward a significantly higher area coverage as it evolves.