Runtime Analysis of Cartesian Genetic Programming in Evolving Boolean Functions
Cartesian Genetic Programming (CGP) is among the practical and popular forms of Genetic Programming as it uses a graph-based representation of programs. This paper presents a first runtime analysis of CGP in evolving Boolean functions using complete training sets. We prove an asymptotic bound $O(n D^5)$ for the expected number of fitness evaluations of CGP to construct a conjunction of $n$ inputs using at most $D \geq n-1$ binary gates, a minimal function set, and even with a strict survival selection. When the non-strict selection is used, the bound is improved to $O(n D^4)$. Our analysis reveals interesting characteristics of CGP induced search, which have been only observed empirically...
| Authors | Duc-Cuong Dang, Roman Kalkreuth, Andre Opris |
|---|---|
| Primary category | cs.NE |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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