# Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms

arXiv: https://arxiv.org/abs/2503.16668 · HTML: https://mlsanity.com/paper/2503.16668/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 1 of 1)

- [In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics](https://mlsanity.com/paper/2410.16309/) (2024)

## Built on by (most cited first, 11 of 11)

- [A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving](https://mlsanity.com/paper/2509.08269/) (2025)
- [Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search](https://mlsanity.com/paper/2504.19636/) (2025)
- [BLADE: Benchmark suite for LLM-driven Automated Design and Evolution of iterative optimisation heuristics](https://mlsanity.com/paper/2504.20183/) (2025)
- [Large Language Models in Operations Research: Methods, Applications, and Challenges](https://mlsanity.com/paper/2509.18180/) (2025)
- [PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs](https://mlsanity.com/paper/2601.20539/) (2026)
- [Behaviour Space Analysis of LLM-driven Meta-heuristic Discovery](https://mlsanity.com/paper/2507.03605/) (2025)
- [Mutation Without Variation: Convergence Dynamics in LLM-Driven Program Evolution](https://mlsanity.com/paper/2606.05408/) (2026)
- [Behavior and Representation in Open-Weight Large Language Models for Combinatorial Optimization: From Feature Extraction to Algorithm Selection](https://mlsanity.com/paper/2512.13374/) (2025)
- [LLaMEA-SAGE: Guiding Automated Algorithm Design with Structural Feedback from Explainable AI](https://mlsanity.com/paper/2601.21511/) (2026)
- [MOSAIC: Adversarial Co-evolution of Specialist Heuristics and Problem Instances for LLM-based Automated Heuristic Design](https://mlsanity.com/paper/2608.07544/) (2026)
- [EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design](https://mlsanity.com/paper/2610.17379/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated great promise in generating code, especially when used inside an evolutionary computation framework to iteratively optimize the generated algorithms. However, in some cases they fail to generate competitive algorithms or the code optimization stalls, and we are left with no recourse because of a lack of understanding of the generation process and generated codes. We present a novel approach to mitigate this problem by enabling users to analyze the generated codes inside the evolutionary process and how they evolve over repeated prompting of the LLM. We show results for three benchmark problem classes and demonstrate novel insights. In particular, LLMs tend to generate more complex code with repeated prompting, but additional complexity can hurt algorithmic performance in some cases. Different LLMs have different coding ``styles'' and generated code tends to be dissimilar to other LLMs. These two findings suggest that using different LLMs inside the code evolution frameworks might produce higher performing code than using only one LLM.