  We demonstrate how an evolutionary algorithm can be extended with a
curriculum learning process that selects automatically the environmental
conditions in which the evolving agents are evaluated. The environmental
conditions are selected so to adjust the level of difficulty to the ability
level of the current evolving agents and so to challenge the weaknesses of the
evolving agents. The method does not require domain knowledge and does not
introduce additional hyperparameters. The results collected on two benchmark
problems, that require to solve a task in significantly varying environmental
conditions, demonstrate that the method proposed outperforms conventional
algorithms and generates solutions that are robust to variations
