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.

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© 2016 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2016
MIT Press
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