More Efficient Evolution of Small Genetic Programs in Cartesian Genetic Programming by Using Genotypic Age

Genetic Programming as an automated method to evolve suitable computer programs for a predefined task can also be applied to multi-objective optimization problems. Originally, Genetic Programming uses tree structures for the representation of a computer program, but further development also enabled a graph based representation called Cartesian Genetic Programming. In the last years, Cartesian Genetic Programming has also been applied to multi-objective optimization problems. For example, we use this representation to determine smaller mathematical expressions or image processing filters with a maximum number of operators. Previous research showed that algorithm stagnation is a common issue in Cartesian Genetic Programming. This behavior comes along with a decrease of diversity in the population and increases the computational effort to find a suitable solution. In this paper, we combine the multi-objective search for smaller genetic programs with an efficient diversity preservation technique. A modified version of the popular NSGA-II algorithm is presented to evolve small programs with a lower amount of fitness evaluations and a higher success rate.