Phased genetic algorithm for simultaneous morphology and controller optimization in soft robots

The tight coupling between morphology, material distribution, actuator layout, and control makes soft robot design a high-dimensional and expensive optimization problem when body and controller are co-designed. In this paper, “co‑design” refers specifically to morphology evolution with per‑candidate reinforcement learning of control, not to the joint genetic evolution of brain and body. We propose a phased genetic algorithm (GA) that evolves voxel‑based morphologies; each candidate is evaluated by training an independent PPO controller under a fixed budget. Three key mechanisms are introduced: 1) a task-aware prior initialization that injects weakly biased, physically meaningful morphology templates into the initial population; 2) a large-block crossover operator (exchanging 2 $$\\times$$ 2 or 3 $$\\times$$ 3 voxel blocks) that preserves functional morphology modules; and 3) a phased evolutionary schedule that balances exploration and stabilization. Compared with four baselines, the proposed method achieves competitive overall performance, with clear advantages on Climber-v0 and Thrower-v0 and faster convergence on Walker-v0. Not applicable.