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.

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