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

Abstract

Self-adaptive parameters are increasingly used in the eld of Evolutionary Robotics, as they allow key evolutionary rates to vary autonomously in a context-sensitive manner throughout the optimisation process. A signi cant limitation 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-only algorithms. is paper focuses on the level at which evolutionary rate restarts are applied in population-based algorithms with >1 evolutionary operator. A er testing on a real hexacopter hovering task, we conclude that individual-level restarting results in higher tness solutions without tness stagnation, and population restarts provide a more stable rate evolution. Without restarts, experiments can become stuck in suboptimal controller/rate combinations which can be di cult to escape from.