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Cartesian Genetic Programming: Benchmarking
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We trained the models with a nodes, offspring over iterations. If the parent reached a score of 0, the evolution process stopped as no prediction error was made on the training dataset. The mutation probability was for the functional nodes and for the outputs. 35 independent runs were made for each experiment for statistical purposes. They were run on a bi Intel Xeon @6140 (218 cores, 2.30 GHz − 3.70 GHz) with 192GB of memory.
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