The field of Embodied Evolution has been strongly developing during the last ten years by more than doubling the yearly number of contributions since 2008 (Bredeche et al., 2018). Many different scenarios and tasks have been addressed and some works have already focus in formalizing and standardizing the paradigm. There hasn’t been a lot of effort, however, towards comparing and improving the performance of the algorithms, which is essential to increase the complexity of the experimental setups and therefore the applicability of the technique. This paper extends the work started in (Trueba, 2017) to compare different variations of EE algorithms with the incorporation of a Differential Evolution based distributed EE algorithm.

Embodied Evolution (EE) is an evolutionary strategy based on natural evolution in which the individuals that make up the population are embodied and situated in an environment where they interact in a local, decentralized and asynchronous fashion. It has been successfully applied in collective problems showing its validity to perform on-line evolution both in simulated and real agents. A key feature of EE is that of emergent specialization, that is, this strategy is able to autonomously generate a distribution of individuals into species if that is advantageous in the scenario. This paper goes in the line of studying such feature in more depth, analyzing how the complexity of the task (fitness landscape) and the complexity of the individuals (control system) affect the emergence of specialization. The analysis is carried out using a canonical EE algorithm in a real problem consisting in a collective surveillance task with simulated Micro Aerial Vehicles.