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

• Published in: Genetic programming and evolvable machines Vol. 22; no. 4; pp. 495 - 509
• Main Authors: Saini, Anil Kumar, Spector, Lee
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Biomedical Engineering and Bioengineering; Compilers; Computer Science; Electrical Engineering; Genetic algorithms; Interpreters; Programming; Programming Languages; Programming Techniques; Software Engineering/Programming and Operating Systems; Special Issue: Highlights of Genetic Programming 2020 Events; Success; Specialists; Loops; Lexicase selection; Parent selection methods
• ISSN: 1389-2576; 1573-7632
• DOI: 10.1007/s10710-021-09417-5

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.