标题： 基于Transformer的语义遗传编程用于符号回归 显示英文标题

标题： Transformer Semantic Genetic Programming for Symbolic Regression

摘要： 在标准遗传编程（stdGP）中，通过修改其语法来产生多样化的解决方案，但其对语义的影响是不确定的。 几何语义遗传编程（GSGP）是GP的一种流行变体，它通过基于线性组合的变化操作有效地搜索语义解空间，尽管这会导致解显著增大。 本文提出了Transformer语义遗传编程（TSGP），这是一种新颖且灵活的语义方法，使用生成式Transformer模型作为搜索算子。 该Transformer在合成测试问题上进行训练，并学习解决方案之间的语义相似性。 一旦模型训练完成，它也可以用于创建与未见过和未知问题具有高语义相似性的后代解决方案。 在多个符号回归问题上的实验表明， TSGP生成的解决方案在预测质量方面与stdGP、SLIM_GSGP、DSR和DAE-GP相当，甚至显著更好。 与SLIM_GSGP类似，TSGP能够在不生成大尺寸解决方案的情况下创建语义相似的新解决方案。 对搜索动态的分析显示，由TSGP生成的解决方案比基准方法生成的解决方案在语义上更加相似，从而能够更好地探索语义解空间。 显示英文摘要

摘要： In standard genetic programming (stdGP), solutions are varied by modifying their syntax, with uncertain effects on their semantics. Geometric-semantic genetic programming (GSGP), a popular variant of GP, effectively searches the semantic solution space using variation operations based on linear combinations, although it results in significantly larger solutions. This paper presents Transformer Semantic Genetic Programming (TSGP), a novel and flexible semantic approach that uses a generative transformer model as search operator. The transformer is trained on synthetic test problems and learns semantic similarities between solutions. Once the model is trained, it can be used to create offspring solutions with high semantic similarity also for unseen and unknown problems. Experiments on several symbolic regression problems show that TSGP generates solutions with comparable or even significantly better prediction quality than stdGP, SLIM_GSGP, DSR, and DAE-GP. Like SLIM_GSGP, TSGP is able to create new solutions that are semantically similar without creating solutions of large size. An analysis of the search dynamic reveals that the solutions generated by TSGP are semantically more similar than the solutions generated by the benchmark approaches allowing a better exploration of the semantic solution space.