Semantic GP is a promising branch of GP that introduces semantic awareness during genetic evolution to improve various aspects of GP. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) that divides the search problem into multiple GP runs. The evolution in each run is guided by a new (dynamic) target based on the residual errors of previous runs. To obtain the final solution, SGP-DT combines the solutions of each run using linear scaling. SGP-DT presents a new methodology to produce the offspring that does not rely on the classic crossover. The synergy between such a methodology and linear scaling yields final solutions with low approximation error and computational cost. We evaluate SGP-DT on eleven well-known data sets and compare with \(\epsilon\)-lexicase, a state-of-the-art evolutionary technique, and seven Machine Learning techniques. SGP-DT achieves small RMSE values, on average 23.19% smaller than the one of \(\epsilon\)-lexicase. Tuning SGP-DT ’s configuration greatly reduces the computational cost while still obtaining competitive results.

语义 GP 是 GP 的一个有前途的分支，它在遗传进化过程中引入语义意识以改进 GP 的各个方面。本文提出了一种新的基于动态目标 (SGP-DT) 的语义 GP 方法，该方法将搜索问题划分为多个 GP 运行。每次运行的演变都由基于先前运行的残差的新（动态）目标引导。为了获得最终解决方案，SGP-DT 使用线性缩放组合每次运行的解决方案。SGP-DT 提出了一种新的方法来产生不依赖于经典交叉的后代。这种方法和线性缩放之间的协同作用产生了具有低近似误差和计算成本的最终解决方案。我们在 11 个众所周知的数据集上评估 SGP-DT 并与\(\epsilon\) - lexicase 进行比较、最先进的进化技术和七种机器学习技术。SGP-DT 实现了小的 RMSE 值，平均比\(\epsilon\) - lexicase小 23.19% 。调整 SGP-DT 的配置大大降低了计算成本，同时仍然获得了有竞争力的结果。