When mating improves on-line collective robotics

It has been long known from the theoretical work on evolution strategies, that recombination improves convergence towards better solution and improves robustness against selection error in noisy environment. We propose to investigate the effect of recombination in online embodied evolutionary robotics, where evolution is decentralized on a swarm of agents. We hypothesize that these properties can also be observed in these algorithms and thus could improve their performance. We introduce the (μ/μ, 1)-On-line EEA which use a recombination operator inspired from evolution strategies and apply it to learn three different collective robotics tasks, locomotion, item collection and item foraging. Different recombination operators are investigated and compared against a purely mutative version of the algorithm. The experiments show that, when correctly designed, recombination improves significantly the adaptation of the swarm in all scenarios.