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.

翻译：我们提出了两种新颖的领域无关遗传算子，它们利用了深度学习的能力：一种用于遗传算法的交叉算子，以及一种用于遗传编程的变异算子。深度神经交叉算子利用深度强化学习和编码器-解码器架构的能力来选择子代基因。BERT变异算子会屏蔽多个遗传编程树节点，然后尝试用最有可能提升个体适应度的节点来替换这些屏蔽位置。我们通过实验证明了两种算子的有效性。