Estimating behavior of a GA-based topology control for self-spreading nodes in MANETs

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.