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 the authors can analyze it in terms of its trajectory in the space of possible populations. They use Vose's theoretical model to calculate the cumulative effects of GA operators of selection, mutation, and crossover as a population evolves through generations.