arXiv:2606.15923v1 Announce Type: cross Abstract: 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
The paper provides a foundational runtime analysis for Cartesian Genetic Programming (CGP) in evolving Boolean functions, which is relevant as AI research increasingly focuses on the efficiency and provable properties of evolutionary algorithms.
A strategic reader should care because improved understanding and analysis of genetic programming's efficiency can lead to more robust and scalable AI systems, impacting fields from automated design to adaptive software.
This research provides a new theoretical bound for CGP's performance, offering a more rigorous basis for selecting and optimizing evolutionary computation methods in practical AI applications.
- · AI researchers
- · Genetic Programming developers
- · Automated design platforms
- · Inefficient AI algorithm developers
The runtime analysis contributes to the theoretical foundations of evolutionary computation.
Improved theoretical understanding could enable more efficient and predictable development of complex AI systems.
These advancements might accelerate the creation of self-improving AI agents or more robust AI-driven discovery platforms.
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Read at arXiv cs.AI