Individual semantics have been used for guiding the learning process of Genetic Programming. Novel genetic operators and different ways of performing parent selection have been proposed with the use of semantics. The latter is the focus of this contribution by proposing three heuristics for parent selection that measure the similarity among individuals' semantics for choosing parents that enhance the addition, Naive Bayes, and Nearest Centroid. To the best of our knowledge, this is the first time that functions' properties are used for guiding the learning process. As the heuristics were created based on the properties of these functions, we apply them only when they are used to create offspring. The similarity functions considered are the cosine similarity, Pearson's correlation, and agreement. We analyze these heuristics' performance against random selection, state-of-the-art selection schemes, and 18 classifiers, including auto-machine-learning techniques, on 30 classification problems with a variable number of samples, variables, and classes. The result indicated that the combination of parent selection based on agreement and random selection to replace an individual in the population produces statistically better results than the classical selection and state-of-the-art schemes, and it is competitive with state-of-the-art classifiers. Finally, the code is released as open-source software.

个体语义已被用于指导遗传编程的学习过程。已经使用语义提出了新的遗传算子和执行父选择的不同方法。后者是这项贡献的重点，它提出了三种用于父选择的启发式方法，这些启发式方法衡量了个人选择增强加法的父级的语义之间的相似性，朴素贝叶斯和最近质心。据我们所知，这是第一次使用函数的属性来指导学习过程。由于启发式是基于这些函数的属性创建的，因此我们仅在它们用于创建后代时应用它们。考虑的相似性函数是余弦相似性、皮尔逊相关性和一致性。我们分析这些启发式 针对随机选择、最先进的选择方案和 18 个分类器（包括自动机器学习技术）在 30 个具有可变数量的样本、变量和类的分类问题上的表现。结果表明，基于协议的父母选择和随机选择相结合来替换种群中的个体，在统计学上比经典选择和最先进的方案产生了更好的结果，并且与最先进的方案具有竞争力。 -艺术分类器。最后，代码作为开源软件发布。结果表明，基于协议的父母选择和随机选择相结合来替换种群中的个体，在统计学上比经典选择和最先进的方案产生了更好的结果，并且与最先进的方案具有竞争力。 -艺术分类器。最后，代码作为开源软件发布。结果表明，基于协议的父母选择和随机选择相结合来替换种群中的个体，在统计学上比经典选择和最先进的方案产生了更好的结果，并且与最先进的方案具有竞争力。 -艺术分类器。最后，代码作为开源软件发布。