Relationships between parent selection methods, looping constructs, and success rate in genetic programming

In genetic programming, parent selection methods are employed to select promising candidate individuals from the current generation that can be used as parents for the next generation. These algorithms can affect, sometimes indirectly, whether or not individuals containing certain programming constructs, such as loops, are selected and propagated in the population. This in turn can affect the chances that the population will produce a solution to the problem. In this paper, we present the results of the experiments using three different parent selection methods on four benchmark program synthesis problems. We analyze the relationships between the selection methods, the numbers of individuals in the population that make use of loops, and success rates. The results show that the support for the selection of specialists is associated both with the use of loops in evolving populations and with higher success rates.