Node Preservation and its Effect on Crossover in Cartesian Genetic Programming

TL;DR

This paper investigates how node preservation techniques in crossover and mutation operators affect the performance of Cartesian Genetic Programming, showing that preserving nodes improves search effectiveness in symbolic regression tasks.

Contribution

It introduces and evaluates node preservation methods in crossover and mutation, demonstrating their positive impact on CGP performance over traditional approaches.

Findings

Node preservation improves search performance in CGP.

Crossover with node preservation outperforms traditional methods.

Node mutation enhances symbolic regression results.

Abstract

While crossover is a critical and often indispensable component in other forms of Genetic Programming, such as Linear- and Tree-based, it has consistently been claimed that it deteriorates search performance in CGP. As a result, a mutation-alone $(1+\lambda)$ evolutionary strategy has become the canonical approach for CGP. Although several operators have been developed that demonstrate an increased performance over the canonical method, a general solution to the problem is still lacking. In this paper, we compare basic crossover methods, namely one-point and uniform, to variants in which nodes are ``preserved,'' including the subgraph crossover developed by Roman Kalkreuth, the difference being that when ``node preservation'' is active, crossover is not allowed to break apart instructions. We also compare a node mutation operator to the traditional point mutation; the former simply replaces an entire node with a new one. We find that node preservation in both mutation and crossover improves search using symbolic regression benchmark problems, moving the field towards a general solution to CGP crossover.