Recent results in evolutionary robotics show that explicitly encouraging the behavioral diversity of candidate solutions drastically improves the convergence of many experiments. The performance of this technique depends, however, on the choice of a behavioral similarity measure (BSM). Here we propose that the experimenter does not actually need to choose: provided that several similarity measures are conceivable, using them all could lead to better results than choosing a single one. Values computed by several BSM can be averaged, which is computationally expensive because it requires the computation of all the BSM at each generation, or randomly switched at a user-chosen frequency, which is a cheaper alternative. We compare these two approaches in two experimental setups – a ball collecting task and hexapod locomotion – with five different BSMs. Results show that (1) using several BSM in a single run increases the performance while avoiding the need to choose the most appropriate BSM and (2) switching between BSMs leads to better results than taking the mean behavioral diversity, while requiring less computational power.