## 2 Related Work  
The first experiment that is considered is the leader selection and preservation in a swarm of static robots. The leader selection is an interesting problem in swarm robotics as the existence of a leader of the group, or from a more general perspective different assigned roles in the swarm, eases the resolution of multiple collective tasks, such as flocking [34; 35], foraging [8], coordinated movement [36] or goal navigation [37] among others. The election of a swarm leader has been treated in several works [27; 30; 32; 38]. Firstly, the leader selection procedure in [38] avoids the usage of direct communication among robots as it only employs the positions of other agents in the neighborhood. A robot becomes a leader whenever all the other robots in its vicinity lie on the same quadrant, considering the robot's position as the origin of coordinates. In [30], the authors designed a voting algorithm based on local communication in a static swarm for the leader election, among other cooperative tasks. The results of their experiments successfully show consensus in the selection in most cases. Alternatively, one of the multiple experiments in [32] is the leader election. They employ Wave Oriented Swarm Paradigm (WOSP) techniques in order to trigger the emergence of collective behaviors, such as leader election, with local binary information exchange. The controllers are also handcrafted as outstandingly simple and compact finite state machines. Neither evolution nor neural controllers are explored in these papers. On the contrary, in [27], the leader selection and role allocation problems are faced using neural controllers and evolutionary computation. The swarm members communicate locally through a communication system and a robot can assume the role of leader by directly maximizing its communication output. In contrast with [27], in our study, we decouple CTRNN outputs for elaborating the communication message and claiming the leadership. Thus, the fitness function does not directly promote or reward the use of the communication channel.  
The borderline identification of a swarm is a relevant problem in the context of collective tasks such as flocking, aggregation or shape formation. The awareness of the limits of the swarm can refine these tasks and potentially aid the avoidance of swarm disconnection. Additionally, the identification of the swarm shape can be harnessed for the estimation of areas (e.g., estimation of the burned area in forest fires). [PERSON] et al. [32] have treated the problem of boundary identification in the context of swarm robotics. They use minimalistic robot behaviors and binary communication using Wave Oriented Swarm Paradigm (WOSP).  
Even though the tasks that are addressed and solved in this paper are remarkably important, the main motivation and contribution of this study is the following. We successfully verify that the same CTRNN controller model, optimized using evolutionary computation, can lead to the emergence of different kinds of communications, either abstract or situated, depending on the problem to be faced. In the case of the leader election problem, an abstract communication emerges while purely situated semantics arise in the case of the borderline identification task. The authors in [39] also addressed this problem, applied to other SR experiments. They compare the optimization of Probabilistic FiniteState Machines (PFSM) and evolutionary algorithms applied to ANN in three different tasks. In both algorithms, the robots are endowed with the capability of broadcasting and receiving messages, whose semantics are initially undefined. They also evolved artificial neural networks using an evolutionary algorithm and the resulting solutions are compared with the behavior and communication of the optimized PFSMs. They report that the optimization of PFSM controllers can generate diverse message semantics in response to the problem to be solved, albeit the emergence of communication was not reached through their evolution process.