An empirical study on the parametrization of cartesian genetic programming
Paul Kaufmann, Roman Kalkreuth · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2017
Since its introduction two decades ago, the way researchers parameterized and optimized Cartesian Genetic Programming (CGP) remained almost unchanged. In this work we investigate non-standard parameterizations and optimization algorithms for CGP. We show that the conventional way of using CGP, i.e. configuring it as a single line optimized by an (1+4) Evolutionary Strategies-style search scheme, is a very good choice but that rectangular CGP geometries and more elaborate metaheuristics, such as Simulated Annealing, can lead to faster convergence rates.