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Self-adaptive parameters are increasingly used in the field of EvolutionaryRobotics, as they allow key evolutionary rates to vary autonomously in acontext-sensitive manner throughout the optimisation process. A significantlimitation to self-adaptive mutation is that rates can be set unfavourably,which hinders convergence. Rate restarts are typically employed to remedy this,but thus far have only been applied in Evolutionary Robotics for mutation-onlyalgorithms. This paper focuses on the level at which evolutionary rate restartsare applied in population-based algorithms with more than 1 evolutionaryoperator. After testing on a real hexacopter hovering task, we conclude thatindividual-level restarting results in higher fitness solutions without fitnessstagnation, and population restarts provide a more stable rate evolution.Without restarts, experiments can become stuck in suboptimal controller/ratecombinations which can be difficult to escape from.

On Self-Adaptive Mutation Restarts for Evolutionary Robotics with Real Rotorcraft