We present two novel domain-independent genetic operators that harness the capabilities of deep learningDeep learning: a crossoverCrossover operator for genetic algorithms and a mutationMutation operator for genetic programming. Deep Neural CrossoverCrossover leverages the capabilities of deep reinforcement learningDeep reinforcement learning and an encoder-decoder architecture to select offspring genes. BERTBERT mutationMutation masks multiple gp-tree nodes and then tries to replace these masks with nodes that will most likely improve the individual’s fitness. We show the efficacy of both operators through experimentation.