Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions
This work addresses efficiency improvements in evolutionary computation for researchers, but it is incremental as it builds on existing methods with specific optimizations.
The paper tackles the efficiency scaling of Cartesian Genetic Programming by preferentially selecting larger solutions among equally good ones, demonstrating advantages in performance and speed across multiple problems, such as reducing the number of evaluations needed for optimal solutions.
We demonstrate how efficiency of Cartesian Genetic Programming method can be scaled up through the preferential selection of phenotypically larger solutions, i.e. through the preferential selection of larger solutions among equally good solutions. The advantage of the preferential selection of larger solutions is validated on the six, seven and eight-bit parity problems, on a dynamically varying problem involving the classification of binary patterns, and on the Paige regression problem. In all cases, the preferential selection of larger solutions provides an advantage in term of the performance of the evolved solutions and in term of speed, the number of evaluations required to evolve optimal or high-quality solutions. The advantage provided by the preferential selection of larger solutions can be further extended by self-adapting the mutation rate through the one-fifth success rule. Finally, for problems like the Paige regression in which neutrality plays a minor role, the advantage of the preferential selection of larger solutions can be extended by preferring larger solutions also among quasi-neutral alternative candidate solutions, i.e. solutions achieving slightly different performance.