Abstract
This paper describes a methodology for analyzing the evolutionary dynamics of genetic programming (GP) using genealogical information, diversity measures and information about the fitness variation from parent to offspring. We introduce a new subtree tracing approach for identifying the origins of genes in the structure of individuals, and we show that only a small fraction of ancestor individuals are responsible for the evolvement of the best solutions in the population.
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It was also possible to use the Jaccard index \(J(A_1,A_2)=\frac{|A_1 \cap A_2|}{|A_1 \cup A_2|}\) as it is very similar to the Sørensen-Dice coefficient. However this choice makes no practical difference for the results presented in this publication.
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Acknowledgments
The work described in this paper was done within the COMET Project Heuristic Optimization in Production and Logistics (HOPL), #843532 funded by the Austrian Research Promotion Agency (FFG).
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Burlacu, B., Affenzeller, M., Kommenda, M. (2015). On the Effectiveness of Genetic Operations in Symbolic Regression. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2015. EUROCAST 2015. Lecture Notes in Computer Science(), vol 9520. Springer, Cham. https://doi.org/10.1007/978-3-319-27340-2_46
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