Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions

TL;DR

This paper shows that selecting larger solutions in Cartesian Genetic Programming enhances performance and speed across various problems, especially when combined with adaptive mutation strategies.

Contribution

It introduces a method of preferentially selecting larger solutions in Cartesian Genetic Programming and demonstrates its effectiveness across multiple benchmark problems.

Findings

Larger solution selection improves performance and speed.

Adaptive mutation further enhances benefits.

Preference among quasi-neutral solutions extends advantages.

Abstract

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.