  In several network problems the optimum behavior of the agents (i.e., the
nodes of the network) is not known before deployment. Furthermore, the agents
might be required to adapt, i.e. change their behavior based on the environment
conditions. In these scenarios, offline optimization is usually costly and
inefficient, while online methods might be more suitable. In this work, we use
a distributed Embodied Evolution approach to optimize spatially distributed,
locally interacting agents by allowing them to exchange their behavior
parameters and learn from each other to adapt to a certain task within a given
environment. Our results on several test scenarios show that the local exchange
of information, performed by means of crossover of behavior parameters with
neighbors, allows the network to conduct the optimization process more
efficiently than the cases where local interactions are not allowed, even when
there are large differences on the optimal behavior parameters within each
agent's neighborhood.
