Modelling Evolvability in Genetic Programming

We develop a tree-based genetic programming system capable of modelling evolvability during evolution through machine learning algorithms, and exploiting those models to increase the efficiency and final fitness. Existing methods of determining evolvability require too much computational time to be effective in any practical sense. By being able to model evolvability instead, computational time may be reduced. This will be done first by demonstrating the effectiveness of modelling these properties a priori, before expanding the system to show its effectiveness as evolution occurs.

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