  We present two novel domain-independent genetic operators that harness the
capabilities of deep learning: a crossover operator for genetic algorithms and
a mutation operator for genetic programming. Deep Neural Crossover leverages
the capabilities of deep reinforcement learning and an encoder-decoder
architecture to select offspring genes. BERT mutation 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.
