论文标题
SGP-DT：基于动态目标的语义遗传编程
SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
论文作者
论文摘要
语义GP是一种有前途的方法，它在遗传进化过程中引入语义意识。本文基于动态目标（SGP-DT）提出了一种新的语义GP方法，该方法将搜索问题分为多个GP运行。每次运行中的演变都以基于残差误差的新（动态）目标为指导。为了获得最终解决方案，SGP-DT使用线性缩放结合了每种运行的解决方案。 SGP-DT提出了一种新方法，以产生不依赖经典跨界的后代。这种方法和线性缩放之间的协同作用与低近似误差和计算成本的最终解决方案产生。我们在八个知名的数据集上评估了SGP-DT，并与最先进的进化技术进行比较。 SGP-DT达到小的RMSE值，平均比ε-肌酶酶之一小23.19％。
Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. 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. 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 to final solutions with low approximation error and computational cost. We evaluate SGP-DT on eight well-known data sets and compare with ε-lexicase, a state-of-the-art evolutionary technique. SGP-DT achieves small RMSE values, on average 23.19% smaller than the one of ε-lexicase.
