Enhancing Cartesian genetic programming through preferential selection of larger solutions

We demonstrate how the efficiency of Cartesian genetic programming methods can be enhanced through the preferential selection of phenotypically larger solutions among equally good solutions. The advantage is demonstrated in two qualitatively different problems: the eight-bit parity problems and the “Paige” regression problem. In both cases, the preferential selection of larger solutions provides an advantage in term of the performance and of speed, i.e. number of evaluations required to evolve optimal or high-quality solutions. Performance can be further enhanced by self-adapting the mutation rate through the one-fifth success rule. Finally, we demonstrate that, for problems like the Paige regression in which neutrality plays a smaller role, performance can be further improved by preferentially selecting larger solutions also among candidates with similar fitness.

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