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.
CITATION STYLE
Kaufmann, P., & Kalkreuth, R. (2017). Parametrizing Cartesian Genetic Programming: An Empirical Study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10505 LNAI, pp. 316–322). Springer Verlag. https://doi.org/10.1007/978-3-319-67190-1_26
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