  Individual's semantics have been used for guiding the learning process of
Genetic Programming solving supervised learning problems. The semantics has
been used to proposed novel genetic operators as well as different ways of
performing parent selection. The latter is the focus of this contribution by
proposing three heuristics for parent selection that replace the fitness
function on the selection mechanism entirely. These heuristics complement
previous work by being inspired in the characteristics of the addition, Naive
Bayes, and Nearest Centroid functions and applying them only when the function
is used to create an offspring. These heuristics use different similarity
measures among the parents to decide which of them is more appropriate given a
function. 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.
